Medical_Cost_Personal_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "Medical Cost Personal Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOTh7YJz2dbl7cqMB20Istd",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/blanpa/Tensorflow-Developer-Certificate-Stuff/blob/main/Medical_Cost_Personal_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDu-qqYV7t2Q"
      },
      "source": [
        "Content Columns\n",
        "\n",
        "age: age of primary beneficiary\n",
        "\n",
        "sex: insurance contractor gender, female, male\n",
        "\n",
        "bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height,\n",
        "objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9\n",
        "\n",
        "children: Number of children covered by health insurance / Number of dependents\n",
        "\n",
        "smoker: Smoking\n",
        "\n",
        "region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.\n",
        "\n",
        "charges: Individual medical costs billed by health insurance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjldKrQT96tK",
        "outputId": "41d0fa93-a575-4a82-dac6-f34b5c170b45"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon May  3 19:16:13 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.19.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   42C    P0    25W / 300W |      0MiB / 16160MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4wGf1CiDkf4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c8df31b-8430-4fcc-8f3e-bda834c3c307"
      },
      "source": [
        "# Load the TensorBoard notebook extension\n",
        "%load_ext tensorboard\n",
        "import datetime, os"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The tensorboard extension is already loaded. To reload it, use:\n",
            "  %reload_ext tensorboard\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhNbCgoe7Sjb"
      },
      "source": [
        "# Import required libaries\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qY3uZXxAPMUO"
      },
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tx5yGk3U8Wcu"
      },
      "source": [
        "# Read the dataset\n",
        "insurance = pd.read_csv(filepath_or_buffer = \"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "hRUIUU4p-lUA",
        "outputId": "b0b5ae43-bee2-4038-f96b-cb3fbd93b1ae"
      },
      "source": [
        "insurance.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex     bmi  children smoker     region      charges\n",
              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
              "1   18    male  33.770         1     no  southeast   1725.55230\n",
              "2   28    male  33.000         3     no  southeast   4449.46200\n",
              "3   33    male  22.705         0     no  northwest  21984.47061\n",
              "4   32    male  28.880         0     no  northwest   3866.85520"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WEuIQHoaHQUz",
        "outputId": "7ea21197-e2ec-43d3-dc6b-c1ac82f8a99b"
      },
      "source": [
        "insurance.info()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1338 non-null   int64  \n",
            " 1   sex       1338 non-null   object \n",
            " 2   bmi       1338 non-null   float64\n",
            " 3   children  1338 non-null   int64  \n",
            " 4   smoker    1338 non-null   object \n",
            " 5   region    1338 non-null   object \n",
            " 6   charges   1338 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "bZaTgezQHTs8",
        "outputId": "363385de-b0f1-4f61-9931-a486d6a7c8b7"
      },
      "source": [
        "insurance.describe()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>39.207025</td>\n",
              "      <td>30.663397</td>\n",
              "      <td>1.094918</td>\n",
              "      <td>13270.422265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.049960</td>\n",
              "      <td>6.098187</td>\n",
              "      <td>1.205493</td>\n",
              "      <td>12110.011237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.960000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1121.873900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>26.296250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4740.287150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9382.033000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>34.693750</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16639.912515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>53.130000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>63770.428010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age          bmi     children       charges\n",
              "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
              "mean     39.207025    30.663397     1.094918  13270.422265\n",
              "std      14.049960     6.098187     1.205493  12110.011237\n",
              "min      18.000000    15.960000     0.000000   1121.873900\n",
              "25%      27.000000    26.296250     0.000000   4740.287150\n",
              "50%      39.000000    30.400000     1.000000   9382.033000\n",
              "75%      51.000000    34.693750     2.000000  16639.912515\n",
              "max      64.000000    53.130000     5.000000  63770.428010"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2leQ9cAEO6rP"
      },
      "source": [
        "## Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "QF5tOzWR8gEE",
        "outputId": "4b550f57-2868-410b-d99b-aac0b05caabd"
      },
      "source": [
        "# One Hot Encoding\n",
        "insurance_one_hot = pd.get_dummies(data = insurance)\n",
        "insurance_one_hot"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>charges</th>\n",
              "      <th>sex_female</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>smoker_no</th>\n",
              "      <th>smoker_yes</th>\n",
              "      <th>region_northeast</th>\n",
              "      <th>region_northwest</th>\n",
              "      <th>region_southeast</th>\n",
              "      <th>region_southwest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>16884.92400</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>1725.55230</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>4449.46200</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>21984.47061</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>3866.85520</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1333</th>\n",
              "      <td>50</td>\n",
              "      <td>30.970</td>\n",
              "      <td>3</td>\n",
              "      <td>10600.54830</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>2205.98080</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>1629.83350</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>21</td>\n",
              "      <td>25.800</td>\n",
              "      <td>0</td>\n",
              "      <td>2007.94500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>29.070</td>\n",
              "      <td>0</td>\n",
              "      <td>29141.36030</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      age     bmi  ...  region_southeast  region_southwest\n",
              "0      19  27.900  ...                 0                 1\n",
              "1      18  33.770  ...                 1                 0\n",
              "2      28  33.000  ...                 1                 0\n",
              "3      33  22.705  ...                 0                 0\n",
              "4      32  28.880  ...                 0                 0\n",
              "...   ...     ...  ...               ...               ...\n",
              "1333   50  30.970  ...                 0                 0\n",
              "1334   18  31.920  ...                 0                 0\n",
              "1335   18  36.850  ...                 1                 0\n",
              "1336   21  25.800  ...                 0                 1\n",
              "1337   61  29.070  ...                 0                 0\n",
              "\n",
              "[1338 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "8OhRPXCUp201",
        "outputId": "9cc90aea-8006-43d1-8992-dc04b5e3efb2"
      },
      "source": [
        "insurance.age.plot(kind=\"hist\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdfdf5ac310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "QcVuj8c9qgEK",
        "outputId": "9f3d8250-4708-426f-f815-911a80669a08"
      },
      "source": [
        "insurance.bmi.plot(kind=\"hist\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdfdf520f10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "a_H2nlDIqinP",
        "outputId": "602b86d6-91dd-4840-c106-2dca8f32e880"
      },
      "source": [
        "insurance.children.plot(kind=\"hist\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdfdf4a7050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD7CAYAAACFfIhNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAARyklEQVR4nO3df8xeZX3H8fdHCuPHVERqR9picWtwZBvYPTIM+6EQN36osE2ZZmpDunXJcMGwZKtmcVuyJfjHRFk2YiduxTkVcUgnzFkRXUwm2GpFfo6OldEKtlN+iLgx9Ls/7qvHh/K03IXn3Kf0fr+SO/d1rnPOfb53CP0813XOfU6qCkmSAJ4zdAGSpP2HoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6vQaCkmOTHJVkjuS3J7kFUmOSrIhyV3t/QVt2yS5NMmWJDcnWdFnbZKkJ+t7pPA+4NNV9VLgROB2YA1wfVUtB65vywBnAsvbazVwWc+1SZJ2k75+vJbk+cBm4CU16yBJ7gReWVX3JTkG+HxVHZ/k/a39kd2329Mxjj766Fq2bFkv9UvSgWrTpk3/XVUL51q3oMfjHgfsBP42yYnAJuBCYNGsf+jvBxa19mLg3ln7b2t9TwiFJKsZjSQ49thj2bhxY29fQJIOREnu2dO6PqePFgArgMuq6mXAd/nhVBEAbQSxT0OVqlpbVTNVNbNw4ZxBJ0l6mvoMhW3Atqq6sS1fxSgkvtmmjWjvO9r67cDSWfsvaX2SpAnpLRSq6n7g3iTHt67TgduA9cDK1rcSuKa11wNvbVchnQI8tLfzCZKk+dfnOQWA3wM+nOQQ4G7gfEZBdGWSVcA9wHlt2+uAs4AtwKNtW0nSBPUaClW1GZiZY9Xpc2xbwAV91iNJ2jt/0SxJ6hgKkqSOoSBJ6hgKkqRO31cf7beWrbl2sGNvvfjswY4tSXvjSEGS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEmdXkMhydYkX0+yOcnG1ndUkg1J7mrvL2j9SXJpki1Jbk6yos/aJElPNomRwquq6qSqmmnLa4Drq2o5cH1bBjgTWN5eq4HLJlCbJGmWIaaPzgHWtfY64NxZ/VfUyJeAI5McM0B9kjS1+g6FAj6TZFOS1a1vUVXd19r3A4taezFw76x9t7W+J0iyOsnGJBt37tzZV92SNJUW9Pz5P19V25O8CNiQ5I7ZK6uqktS+fGBVrQXWAszMzOzTvpKkvet1pFBV29v7DuBq4GTgm7umhdr7jrb5dmDprN2XtD5J0oT0FgpJjkjy3F1t4JeBW4D1wMq22UrgmtZeD7y1XYV0CvDQrGkmSdIE9Dl9tAi4Osmu4/xDVX06yZeBK5OsAu4BzmvbXwecBWwBHgXO77E2SdIceguFqrobOHGO/m8Bp8/RX8AFfdUjSXpq/qJZktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktTpPRSSHJTkq0k+1ZaPS3Jjki1JPpbkkNb/I215S1u/rO/aJElPNImRwoXA7bOW3w1cUlU/ATwArGr9q4AHWv8lbTtJ0gT1GgpJlgBnAx9oywFOA65qm6wDzm3tc9oybf3pbXtJ0oT0PVJ4L/AHwA/a8guBB6vq8ba8DVjc2ouBewHa+ofa9k+QZHWSjUk27ty5s8/aJWnq9BYKSV4D7KiqTfP5uVW1tqpmqmpm4cKF8/nRkjT1FvT42acCr0tyFnAo8DzgfcCRSRa00cASYHvbfjuwFNiWZAHwfOBbPdYnSdpNbyOFqnpHVS2pqmXAG4HPVdVvAjcAr2+brQSuae31bZm2/nNVVX3VJ0l6siF+p/CHwEVJtjA6Z3B5678ceGHrvwhYM0BtkjTV+pw+6lTV54HPt/bdwMlzbPM/wBsmUY8kaW7+olmS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1BkrFJL8dN+FSJKGN+5I4a+T3JTkd5M8v9eKJEmDGSsUquoXgN9kdMO6TUn+Icmre61MkjRxY59TqKq7gD9idO+iXwIuTXJHkl/rqzhJ0mSNe07hZ5JcwuixmqcBr62qn2ztS3qsT5I0QePeEO8vGT1S851V9b1dnVX1jSR/1EtlkqSJGzcUzga+V1XfB0jyHODQqnq0qj7UW3WSpIka95zCZ4HDZi0f3vokSQeQcUPh0Kp6ZNdCax/eT0mSpKGMGwrfTbJi10KSnwW+t5ftJUnPQuOeU3g78PEk3wAC/BjwG71VJUkaxFihUFVfTvJS4PjWdWdV/V9/ZUmShrAvz2h+ObCs7bMiCVV1RS9VSZIGMVYoJPkQ8OPAZuD7rbsAQ0GSDiDjjhRmgBOqqvosRpI0rHFD4RZGJ5fv67EW9WzZmmsHO/bWi88e7NiSxjduKBwN3JbkJuB/d3VW1et6qUqSNIhxQ+FP+ixCkrR/GPeS1C8keTGwvKo+m+Rw4KB+S5MkTdq4t87+beAq4P2tazHwyb6KkiQNY9zbXFwAnAo8DN0Dd160tx2SHNoe4fm1JLcm+dPWf1ySG5NsSfKxJIe0/h9py1va+mVP90tJkp6ecUPhf6vqsV0LSRYw+p3CXvcBTquqE4GTgDOSnAK8G7ikqn4CeABY1bZfBTzQ+i9p20mSJmjcUPhCkncCh7VnM38c+Ke97VAju+6senB7FaOntV3V+tcB57b2OW2Ztv70JBmzPknSPBg3FNYAO4GvA78DXMfoec17leSgJJuBHcAG4D+AB6vq8bbJNkbnJ2jv9wK09Q8BLxyzPknSPBj36qMfAH/TXmNrT2o7KcmRwNXAS/e5wt0kWQ2sBjj22GOf6cdJkmYZ995H/8kc5xCq6iXj7F9VDya5AXgFcGSSBW00sATY3jbbDiwFtrVzFs8HvjXHZ60F1gLMzMx42w1Jmkf7cu+jXQ4F3gActbcdkiwE/q8FwmHAqxmdPL4BeD3wUWAlcE3bZX1b/re2/nPea0mSJmvc6aPd/2J/b5JNwLv2stsxwLokBzE6d3FlVX0qyW3AR5P8GfBV4PK2/eXAh5JsAb4NvHEfvockaR6MO320YtbicxiNHPa6b1XdDLxsjv67gZPn6P8fRiMQSdJAxp0++otZ7ceBrcB5816NJGlQ404fvarvQiRJwxt3+uiiva2vqvfMTzmSpCHty9VHL2d0hRDAa4GbgLv6KEqSNIxxQ2EJsKKqvgOQ5E+Aa6vqzX0VJkmavHFvc7EIeGzW8mOtT5J0ABl3pHAFcFOSq9vyufzw5nWSpAPEuFcf/XmSfwZ+oXWdX1Vf7a8sSdIQxp0+AjgceLiq3sfo/kTH9VSTJGkg4z6O84+BPwTe0boOBv6+r6IkScMYd6Twq8DrgO8CVNU3gOf2VZQkaRjjhsJj7Y6lBZDkiP5KkiQNZdxQuDLJ+xk9C+G3gc+yjw/ckSTt/57y6qP2nOSPMXpq2sPA8cC7qmpDz7VJkibsKUOhqirJdVX104yesyxJOkCNO330lSQv77USSdLgxv1F888Bb06yldEVSGE0iPiZvgqT5sOyNdcOctytF589yHGlZ2qvoZDk2Kr6L+BXJlSPJGlATzVS+CSju6Pek+QTVfXrkyhKkjSMpzqnkFntl/RZiCRpeE8VCrWHtiTpAPRU00cnJnmY0YjhsNaGH55ofl6v1UmSJmqvoVBVB02qEEnS8Pbl1tmSpAOcoSBJ6hgKkqSOoSBJ6hgKkqROb6GQZGmSG5LcluTWJBe2/qOSbEhyV3t/QetPkkuTbElyc5IVfdUmSZpbnyOFx4Hfr6oTgFOAC5KcAKwBrq+q5cD1bRngTGB5e60GLuuxNknSHHoLhaq6r6q+0trfAW4HFgPnAOvaZuuAc1v7HOCKGvkSo6e8HdNXfZKkJ5vIOYUky4CXATcCi6rqvrbqfmBRay8G7p2127bWt/tnrU6yMcnGnTt39lazJE2j3kMhyY8CnwDeXlUPz15XVcU+3lOpqtZW1UxVzSxcuHAeK5Uk9RoKSQ5mFAgfrqp/bN3f3DUt1N53tP7twNJZuy9pfZKkCenz6qMAlwO3V9V7Zq1aD6xs7ZXANbP639quQjoFeGjWNJMkaQLGfRzn03Eq8Bbg60k2t753AhcDVyZZBdwDnNfWXQecBWwBHgXO77E2SdIceguFqvoiT3xIz2ynz7F9ARf0VY8k6an5i2ZJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUqfP21xIGsCyNdcOctytF589yHE1vxwpSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6vYVCkg8m2ZHklll9RyXZkOSu9v6C1p8klybZkuTmJCv6qkuStGd9jhT+Djhjt741wPVVtRy4vi0DnAksb6/VwGU91iVJ2oPeQqGq/hX49m7d5wDrWnsdcO6s/itq5EvAkUmO6as2SdLcJn1OYVFV3dfa9wOLWnsxcO+s7ba1vidJsjrJxiQbd+7c2V+lkjSFBjvRXFUF1NPYb21VzVTVzMKFC3uoTJKm16RD4Zu7poXa+47Wvx1YOmu7Ja1PkjRBkw6F9cDK1l4JXDOr/63tKqRTgIdmTTNJkiZkQV8fnOQjwCuBo5NsA/4YuBi4Mskq4B7gvLb5dcBZwBbgUeD8vuqSJO1Zb6FQVW/aw6rT59i2gAv6qkWSNB5/0SxJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6iwYugBJeqaWrbl2kONuvfjsQY7bJ0cKkqSOIwVJepqGGqFAf6MURwqSpI6hIEnqGAqSpM5+FQpJzkhyZ5ItSdYMXY8kTZv9JhSSHAT8FXAmcALwpiQnDFuVJE2X/SYUgJOBLVV1d1U9BnwUOGfgmiRpqqSqhq4BgCSvB86oqt9qy28Bfq6q3rbbdquB1W3xeODOp3nIo4H/fpr7Plv5naeD33k6PJPv/OKqWjjXimfd7xSqai2w9pl+TpKNVTUzDyU9a/idp4PfeTr09Z33p+mj7cDSWctLWp8kaUL2p1D4MrA8yXFJDgHeCKwfuCZJmir7zfRRVT2e5G3AvwAHAR+sqlt7POQznoJ6FvI7Twe/83To5TvvNyeaJUnD25+mjyRJAzMUJEmdqQyFabudRpIPJtmR5Jaha5mUJEuT3JDktiS3Jrlw6Jr6luTQJDcl+Vr7zn86dE2TkOSgJF9N8qmha5mEJFuTfD3J5iQb5/3zp+2cQrudxr8Drwa2Mbrq6U1VddughfUoyS8CjwBXVNVPDV3PJCQ5Bjimqr6S5LnAJuDcA/y/c4AjquqRJAcDXwQurKovDVxar5JcBMwAz6uq1wxdT9+SbAVmqqqXH+tN40hh6m6nUVX/Cnx76Domqaruq6qvtPZ3gNuBxcNW1a8aeaQtHtxeB/RffUmWAGcDHxi6lgPFNIbCYuDeWcvbOMD/sZh2SZYBLwNuHLaS/rWplM3ADmBDVR3o3/m9wB8APxi6kAkq4DNJNrXb/syraQwFTZEkPwp8Anh7VT08dD19q6rvV9VJjO4IcHKSA3a6MMlrgB1VtWnoWibs56tqBaM7Sl/QpofnzTSGgrfTmBJtXv0TwIer6h+HrmeSqupB4AbgjKFr6dGpwOvaHPtHgdOS/P2wJfWvqra39x3A1YymxOfNNIaCt9OYAu2k6+XA7VX1nqHrmYQkC5Mc2dqHMbqY4o5hq+pPVb2jqpZU1TJG/x9/rqrePHBZvUpyRLtwgiRHAL8MzOtVhVMXClX1OLDrdhq3A1f2fDuNwSX5CPBvwPFJtiVZNXRNE3Aq8BZGfz1ubq+zhi6qZ8cANyS5mdEfPxuqaiou05wii4AvJvkacBNwbVV9ej4PMHWXpEqS9mzqRgqSpD0zFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktT5f1/Xy/y7RuFSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VjZO1fasPCxZ"
      },
      "source": [
        "Normalization and standardization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9a0zfEu9PDL7"
      },
      "source": [
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "\n",
        "# Column transformer\n",
        "ct = make_column_transformer(\n",
        "    (MinMaxScaler(), [\"age\", \"bmi\", \"children\"]),\n",
        "    (OneHotEncoder(handle_unknown=\"ignore\"), [\"sex\", \"smoker\", \"region\"])\n",
        ")\n",
        "\n",
        "# Train_test_Split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X = insurance.drop(\"charges\", axis = 1)\n",
        "y = insurance.charges\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)   \n",
        "\n",
        "# fit Column Transformer\n",
        "ct.fit(X_train)\n",
        "\n",
        "# Transform training and test data with normalization (Minm´MaxScaler) and OneHOteEncoder\n",
        "X_train_normal = ct.transform(X_train)\n",
        "X_test_normal = ct.transform(X_test)\n"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wh_exhq4O-gi"
      },
      "source": [
        "## Machine Learning Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_8Qua-w_HjX",
        "outputId": "84278ff0-c45a-457a-e750-79c02ba08dcb"
      },
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "model1 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(150),\n",
        "    tf.keras.layers.Dense(200),\n",
        "    tf.keras.layers.Dense(150),\n",
        "    tf.keras.layers.Dense(200),\n",
        "    tf.keras.layers.Dense(150),\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(50),\n",
        "    tf.keras.layers.Dense(25),\n",
        "    tf.keras.layers.Dense(5),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model1.compile(loss=tf.keras.losses.mae,\n",
        "               optimizer=tf.keras.optimizers.Adam(\n",
        "                   learning_rate=0.001\n",
        "               ),\n",
        "               metrics = [\n",
        "                    tf.keras.metrics.mean_absolute_error,\n",
        "                    #tf.keras.metrics.mean_squared_error,\n",
        "                    #tf.keras.metrics.hinge\n",
        "                    ])\n",
        "\n",
        "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
        "\n",
        "history = model1.fit(X_train_normal, y_train, epochs = 100, callbacks=[tensorboard_callback], validation_split=0.3)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "24/24 [==============================] - 1s 25ms/step - loss: 13191.8547 - mean_absolute_error: 13191.8547 - val_loss: 9275.3252 - val_mean_absolute_error: 9275.3252\n",
            "Epoch 2/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 8532.3683 - mean_absolute_error: 8532.3683 - val_loss: 6731.0391 - val_mean_absolute_error: 6731.0391\n",
            "Epoch 3/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 6136.9457 - mean_absolute_error: 6136.9457 - val_loss: 4087.1636 - val_mean_absolute_error: 4087.1636\n",
            "Epoch 4/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4285.1408 - mean_absolute_error: 4285.1408 - val_loss: 3562.1328 - val_mean_absolute_error: 3562.1328\n",
            "Epoch 5/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3730.5146 - mean_absolute_error: 3730.5146 - val_loss: 3430.9485 - val_mean_absolute_error: 3430.9485\n",
            "Epoch 6/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3789.9959 - mean_absolute_error: 3789.9959 - val_loss: 3530.6792 - val_mean_absolute_error: 3530.6792\n",
            "Epoch 7/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4212.0383 - mean_absolute_error: 4212.0383 - val_loss: 3568.0144 - val_mean_absolute_error: 3568.0144\n",
            "Epoch 8/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3684.2395 - mean_absolute_error: 3684.2395 - val_loss: 3308.9253 - val_mean_absolute_error: 3308.9253\n",
            "Epoch 9/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3409.6206 - mean_absolute_error: 3409.6206 - val_loss: 3955.1775 - val_mean_absolute_error: 3955.1775\n",
            "Epoch 10/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3943.0448 - mean_absolute_error: 3943.0448 - val_loss: 3340.4902 - val_mean_absolute_error: 3340.4902\n",
            "Epoch 11/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3919.3496 - mean_absolute_error: 3919.3496 - val_loss: 3406.1565 - val_mean_absolute_error: 3406.1565\n",
            "Epoch 12/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3562.7950 - mean_absolute_error: 3562.7950 - val_loss: 3816.1768 - val_mean_absolute_error: 3816.1768\n",
            "Epoch 13/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3645.6024 - mean_absolute_error: 3645.6024 - val_loss: 3368.3652 - val_mean_absolute_error: 3368.3652\n",
            "Epoch 14/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3591.8489 - mean_absolute_error: 3591.8489 - val_loss: 3466.2991 - val_mean_absolute_error: 3466.2991\n",
            "Epoch 15/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3620.1177 - mean_absolute_error: 3620.1177 - val_loss: 3534.9214 - val_mean_absolute_error: 3534.9214\n",
            "Epoch 16/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3666.5258 - mean_absolute_error: 3666.5258 - val_loss: 3436.7427 - val_mean_absolute_error: 3436.7427\n",
            "Epoch 17/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3469.6466 - mean_absolute_error: 3469.6466 - val_loss: 3294.4275 - val_mean_absolute_error: 3294.4275\n",
            "Epoch 18/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4019.7859 - mean_absolute_error: 4019.7859 - val_loss: 3532.2656 - val_mean_absolute_error: 3532.2656\n",
            "Epoch 19/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3737.9784 - mean_absolute_error: 3737.9784 - val_loss: 3352.5400 - val_mean_absolute_error: 3352.5400\n",
            "Epoch 20/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3620.7089 - mean_absolute_error: 3620.7089 - val_loss: 3782.2979 - val_mean_absolute_error: 3782.2979\n",
            "Epoch 21/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3868.1128 - mean_absolute_error: 3868.1128 - val_loss: 3419.3154 - val_mean_absolute_error: 3419.3154\n",
            "Epoch 22/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3598.3826 - mean_absolute_error: 3598.3826 - val_loss: 3360.0425 - val_mean_absolute_error: 3360.0425\n",
            "Epoch 23/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3640.5820 - mean_absolute_error: 3640.5820 - val_loss: 3406.0339 - val_mean_absolute_error: 3406.0339\n",
            "Epoch 24/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3874.8853 - mean_absolute_error: 3874.8853 - val_loss: 3648.7371 - val_mean_absolute_error: 3648.7371\n",
            "Epoch 25/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3538.3372 - mean_absolute_error: 3538.3372 - val_loss: 3602.7505 - val_mean_absolute_error: 3602.7505\n",
            "Epoch 26/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3934.5348 - mean_absolute_error: 3934.5348 - val_loss: 3320.7539 - val_mean_absolute_error: 3320.7539\n",
            "Epoch 27/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3863.2266 - mean_absolute_error: 3863.2266 - val_loss: 3486.5981 - val_mean_absolute_error: 3486.5981\n",
            "Epoch 28/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3662.0797 - mean_absolute_error: 3662.0797 - val_loss: 3544.4587 - val_mean_absolute_error: 3544.4587\n",
            "Epoch 29/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3904.1704 - mean_absolute_error: 3904.1704 - val_loss: 3330.5803 - val_mean_absolute_error: 3330.5803\n",
            "Epoch 30/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3627.4673 - mean_absolute_error: 3627.4673 - val_loss: 3451.1958 - val_mean_absolute_error: 3451.1958\n",
            "Epoch 31/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4182.4798 - mean_absolute_error: 4182.4798 - val_loss: 3549.9668 - val_mean_absolute_error: 3549.9668\n",
            "Epoch 32/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3410.7770 - mean_absolute_error: 3410.7770 - val_loss: 3335.8274 - val_mean_absolute_error: 3335.8274\n",
            "Epoch 33/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3562.2181 - mean_absolute_error: 3562.2181 - val_loss: 3452.6440 - val_mean_absolute_error: 3452.6440\n",
            "Epoch 34/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3958.8112 - mean_absolute_error: 3958.8112 - val_loss: 3777.7021 - val_mean_absolute_error: 3777.7021\n",
            "Epoch 35/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4125.1399 - mean_absolute_error: 4125.1399 - val_loss: 3411.5764 - val_mean_absolute_error: 3411.5764\n",
            "Epoch 36/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3583.9049 - mean_absolute_error: 3583.9049 - val_loss: 3927.0774 - val_mean_absolute_error: 3927.0774\n",
            "Epoch 37/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3775.1996 - mean_absolute_error: 3775.1996 - val_loss: 3512.0078 - val_mean_absolute_error: 3512.0078\n",
            "Epoch 38/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3738.9630 - mean_absolute_error: 3738.9630 - val_loss: 3485.7236 - val_mean_absolute_error: 3485.7236\n",
            "Epoch 39/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3746.3026 - mean_absolute_error: 3746.3026 - val_loss: 3331.9285 - val_mean_absolute_error: 3331.9285\n",
            "Epoch 40/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3559.6417 - mean_absolute_error: 3559.6417 - val_loss: 3342.0608 - val_mean_absolute_error: 3342.0608\n",
            "Epoch 41/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3531.2487 - mean_absolute_error: 3531.2487 - val_loss: 3360.7659 - val_mean_absolute_error: 3360.7659\n",
            "Epoch 42/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3736.1779 - mean_absolute_error: 3736.1779 - val_loss: 3286.6013 - val_mean_absolute_error: 3286.6013\n",
            "Epoch 43/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3561.8971 - mean_absolute_error: 3561.8971 - val_loss: 3532.5137 - val_mean_absolute_error: 3532.5137\n",
            "Epoch 44/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3920.8759 - mean_absolute_error: 3920.8759 - val_loss: 3418.7244 - val_mean_absolute_error: 3418.7244\n",
            "Epoch 45/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3669.6001 - mean_absolute_error: 3669.6001 - val_loss: 3329.6331 - val_mean_absolute_error: 3329.6331\n",
            "Epoch 46/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3673.6204 - mean_absolute_error: 3673.6204 - val_loss: 3349.2146 - val_mean_absolute_error: 3349.2146\n",
            "Epoch 47/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4240.3075 - mean_absolute_error: 4240.3075 - val_loss: 3635.5005 - val_mean_absolute_error: 3635.5005\n",
            "Epoch 48/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3832.0004 - mean_absolute_error: 3832.0004 - val_loss: 3317.1389 - val_mean_absolute_error: 3317.1389\n",
            "Epoch 49/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3524.3649 - mean_absolute_error: 3524.3649 - val_loss: 3422.5371 - val_mean_absolute_error: 3422.5371\n",
            "Epoch 50/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3562.7632 - mean_absolute_error: 3562.7632 - val_loss: 3310.3828 - val_mean_absolute_error: 3310.3828\n",
            "Epoch 51/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3571.1735 - mean_absolute_error: 3571.1735 - val_loss: 3796.5120 - val_mean_absolute_error: 3796.5120\n",
            "Epoch 52/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3820.5297 - mean_absolute_error: 3820.5297 - val_loss: 3375.3364 - val_mean_absolute_error: 3375.3364\n",
            "Epoch 53/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3559.7418 - mean_absolute_error: 3559.7418 - val_loss: 3446.8625 - val_mean_absolute_error: 3446.8625\n",
            "Epoch 54/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 4026.4017 - mean_absolute_error: 4026.4017 - val_loss: 3364.1301 - val_mean_absolute_error: 3364.1301\n",
            "Epoch 55/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4110.3752 - mean_absolute_error: 4110.3752 - val_loss: 3364.8188 - val_mean_absolute_error: 3364.8188\n",
            "Epoch 56/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4158.4938 - mean_absolute_error: 4158.4938 - val_loss: 3338.6448 - val_mean_absolute_error: 3338.6448\n",
            "Epoch 57/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3734.9472 - mean_absolute_error: 3734.9472 - val_loss: 3718.7212 - val_mean_absolute_error: 3718.7212\n",
            "Epoch 58/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3934.7232 - mean_absolute_error: 3934.7232 - val_loss: 3322.0896 - val_mean_absolute_error: 3322.0896\n",
            "Epoch 59/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3686.9409 - mean_absolute_error: 3686.9409 - val_loss: 3302.8267 - val_mean_absolute_error: 3302.8267\n",
            "Epoch 60/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3739.7656 - mean_absolute_error: 3739.7656 - val_loss: 3349.6211 - val_mean_absolute_error: 3349.6211\n",
            "Epoch 61/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3695.8584 - mean_absolute_error: 3695.8584 - val_loss: 3302.9485 - val_mean_absolute_error: 3302.9485\n",
            "Epoch 62/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3725.8743 - mean_absolute_error: 3725.8743 - val_loss: 3319.9490 - val_mean_absolute_error: 3319.9490\n",
            "Epoch 63/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3329.6162 - mean_absolute_error: 3329.6162 - val_loss: 3291.2024 - val_mean_absolute_error: 3291.2024\n",
            "Epoch 64/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3688.6672 - mean_absolute_error: 3688.6672 - val_loss: 3403.5935 - val_mean_absolute_error: 3403.5935\n",
            "Epoch 65/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3453.4422 - mean_absolute_error: 3453.4422 - val_loss: 3310.5276 - val_mean_absolute_error: 3310.5276\n",
            "Epoch 66/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3719.6303 - mean_absolute_error: 3719.6303 - val_loss: 3384.9380 - val_mean_absolute_error: 3384.9380\n",
            "Epoch 67/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3735.0108 - mean_absolute_error: 3735.0108 - val_loss: 3307.9004 - val_mean_absolute_error: 3307.9004\n",
            "Epoch 68/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3773.3110 - mean_absolute_error: 3773.3110 - val_loss: 3471.2100 - val_mean_absolute_error: 3471.2100\n",
            "Epoch 69/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3520.1684 - mean_absolute_error: 3520.1684 - val_loss: 3421.9678 - val_mean_absolute_error: 3421.9678\n",
            "Epoch 70/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3357.0729 - mean_absolute_error: 3357.0729 - val_loss: 3453.5647 - val_mean_absolute_error: 3453.5647\n",
            "Epoch 71/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3809.6948 - mean_absolute_error: 3809.6948 - val_loss: 3314.8542 - val_mean_absolute_error: 3314.8542\n",
            "Epoch 72/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3890.8128 - mean_absolute_error: 3890.8128 - val_loss: 3388.9763 - val_mean_absolute_error: 3388.9763\n",
            "Epoch 73/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3353.6605 - mean_absolute_error: 3353.6605 - val_loss: 3343.1775 - val_mean_absolute_error: 3343.1775\n",
            "Epoch 74/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3425.6641 - mean_absolute_error: 3425.6641 - val_loss: 3281.1340 - val_mean_absolute_error: 3281.1340\n",
            "Epoch 75/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3972.5131 - mean_absolute_error: 3972.5131 - val_loss: 3418.8416 - val_mean_absolute_error: 3418.8416\n",
            "Epoch 76/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3749.5300 - mean_absolute_error: 3749.5300 - val_loss: 3303.2920 - val_mean_absolute_error: 3303.2920\n",
            "Epoch 77/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3715.1525 - mean_absolute_error: 3715.1525 - val_loss: 3271.9541 - val_mean_absolute_error: 3271.9541\n",
            "Epoch 78/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3617.2764 - mean_absolute_error: 3617.2764 - val_loss: 3608.9131 - val_mean_absolute_error: 3608.9131\n",
            "Epoch 79/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3897.0819 - mean_absolute_error: 3897.0819 - val_loss: 3305.8149 - val_mean_absolute_error: 3305.8149\n",
            "Epoch 80/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3788.4344 - mean_absolute_error: 3788.4344 - val_loss: 3278.7107 - val_mean_absolute_error: 3278.7107\n",
            "Epoch 81/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3740.0096 - mean_absolute_error: 3740.0096 - val_loss: 3435.8552 - val_mean_absolute_error: 3435.8552\n",
            "Epoch 82/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3572.7384 - mean_absolute_error: 3572.7384 - val_loss: 3486.2390 - val_mean_absolute_error: 3486.2390\n",
            "Epoch 83/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3353.1974 - mean_absolute_error: 3353.1974 - val_loss: 3296.1865 - val_mean_absolute_error: 3296.1865\n",
            "Epoch 84/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3642.0495 - mean_absolute_error: 3642.0495 - val_loss: 3389.7773 - val_mean_absolute_error: 3389.7773\n",
            "Epoch 85/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3826.7762 - mean_absolute_error: 3826.7762 - val_loss: 3528.2034 - val_mean_absolute_error: 3528.2034\n",
            "Epoch 86/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4257.4771 - mean_absolute_error: 4257.4771 - val_loss: 3322.9949 - val_mean_absolute_error: 3322.9949\n",
            "Epoch 87/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3748.0950 - mean_absolute_error: 3748.0950 - val_loss: 3326.6785 - val_mean_absolute_error: 3326.6785\n",
            "Epoch 88/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3492.4150 - mean_absolute_error: 3492.4150 - val_loss: 3364.6885 - val_mean_absolute_error: 3364.6885\n",
            "Epoch 89/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3769.1845 - mean_absolute_error: 3769.1845 - val_loss: 3349.8293 - val_mean_absolute_error: 3349.8293\n",
            "Epoch 90/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3896.5334 - mean_absolute_error: 3896.5334 - val_loss: 3392.7690 - val_mean_absolute_error: 3392.7690\n",
            "Epoch 91/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3851.0216 - mean_absolute_error: 3851.0216 - val_loss: 3375.7034 - val_mean_absolute_error: 3375.7034\n",
            "Epoch 92/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3983.1212 - mean_absolute_error: 3983.1212 - val_loss: 3560.1008 - val_mean_absolute_error: 3560.1008\n",
            "Epoch 93/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3577.3057 - mean_absolute_error: 3577.3057 - val_loss: 3536.2278 - val_mean_absolute_error: 3536.2278\n",
            "Epoch 94/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4165.6487 - mean_absolute_error: 4165.6487 - val_loss: 3417.1582 - val_mean_absolute_error: 3417.1582\n",
            "Epoch 95/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3947.6530 - mean_absolute_error: 3947.6530 - val_loss: 3378.2351 - val_mean_absolute_error: 3378.2351\n",
            "Epoch 96/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3770.8130 - mean_absolute_error: 3770.8130 - val_loss: 3337.7119 - val_mean_absolute_error: 3337.7119\n",
            "Epoch 97/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 3304.7924 - mean_absolute_error: 3304.7924 - val_loss: 3368.9463 - val_mean_absolute_error: 3368.9463\n",
            "Epoch 98/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3432.4038 - mean_absolute_error: 3432.4038 - val_loss: 3340.6382 - val_mean_absolute_error: 3340.6382\n",
            "Epoch 99/100\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 4202.3832 - mean_absolute_error: 4202.3832 - val_loss: 3437.1216 - val_mean_absolute_error: 3437.1216\n",
            "Epoch 100/100\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 3611.4520 - mean_absolute_error: 3611.4520 - val_loss: 3443.5535 - val_mean_absolute_error: 3443.5535\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rGkGqpml_oFx",
        "outputId": "c9a3532c-a2df-430c-d8e7-6c1fc2687bb4"
      },
      "source": [
        "model1.evaluate(X_test_normal, y_test)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 3187.6953 - mean_absolute_error: 3187.6953\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3187.6953125, 3187.6953125]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UovAjMNoMGyv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "2dec5b70-3fa6-418c-b91e-31f947208f48"
      },
      "source": [
        "# plot history\n",
        "pd.DataFrame(history.history).plot(figsize=(14,7))\n",
        "plt.ylabel(\"loss\")\n",
        "plt.xlabel(\"epochs\")"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epochs')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8Vq2VNLBu1K"
      },
      "source": [
        "%tensorboard --logdir logs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RK9Y_6dFD5kN"
      },
      "source": [
        "from tensorboard import notebook\n",
        "notebook.list() # View open TensorBoard instances"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btlFnQTaD9A0"
      },
      "source": [
        "#notebook.display(port=6006, height=1000) "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}