linearregressionexample.ipynb

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        "<a href=\"https://colab.research.google.com/github/fikhan/BlockChain_NFTMarketPlace/blob/main/linearregressionexample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Linear Regression Health Costs Calculator\n",
        "Note\n",
        "Damn, I hate this problem! I feel so stupid to keep tweaking the numbers and model layers until I get the expected result. Even so, it doesn't always work. This is why I don't go with Data Science although in love Math. In Math, we're able to prove the correctness. In Data Science, we (or is it just me) keep repeating the same method and hope for a better result. When it works, God know why (I don't believe in God, btw).\n",
        "\n",
        "Total time for this: 2h.\n",
        "\n",
        "Problem description\n",
        "Copied and modfied from this Google Colab link\n",
        "\n",
        "In this challenge, you will predict healthcare costs using a regression algorithm.\n",
        "\n",
        "You are given a dataset that contains information about different people including their healthcare costs. Use the data to predict healthcare costs based on new data.\n",
        "\n",
        "The first two cells of this notebook import libraries and the data.\n",
        "\n",
        "Make sure to convert categorical data to numbers. Use 80% of the data as the train_dataset and 20% of the data as the test_dataset.\n",
        "\n",
        "pop off the \"expenses\" column from these datasets to create new datasets called train_labels and test_labels. Use these labels when training your model.\n",
        "\n",
        "Create a model and train it with the train_dataset. Run the final cell in this notebook to check your model. The final cell will use the unseen test_dataset to check how well the model generalizes.\n",
        "\n",
        "To pass the challenge, model.evaluate must return a Mean Absolute Error of under 3500. This means it predicts health care costs correctly within $3500.\n",
        "\n",
        "The final cell will also predict expenses using the test_dataset and graph the results.\n",
        "\n",
        "Solution\n",
        "Download data"
      ],
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      "outputs": [
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          "text": [
            "--2023-10-31 18:52:26--  https://cdn.freecodecamp.org/project-data/health-costs/insurance.csv\n",
            "Resolving cdn.freecodecamp.org (cdn.freecodecamp.org)... 172.67.70.149, 104.26.3.33, 104.26.2.33, ...\n",
            "Connecting to cdn.freecodecamp.org (cdn.freecodecamp.org)|172.67.70.149|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 50264 (49K) [text/csv]\n",
            "Saving to: ‘insurance.csv’\n",
            "\n",
            "\rinsurance.csv         0%[                    ]       0  --.-KB/s               \rinsurance.csv       100%[===================>]  49.09K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2023-10-31 18:52:26 (4.52 MB/s) - ‘insurance.csv’ saved [50264/50264]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -N https://cdn.freecodecamp.org/project-data/health-costs/insurance.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install tensorflow docs"
      ],
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        "id": "gkfhjX7YDkeh"
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    {
      "cell_type": "code",
      "source": [
        "!pip install -q git+https://github.com/tensorflow/docs"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zGvQ1xNZDowl",
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      },
      "execution_count": 2,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for tensorflow-docs (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "import tensorflow_docs as tfdocs\n",
        "import tensorflow_docs.plots\n",
        "import tensorflow_docs.modeling"
      ],
      "metadata": {
        "id": "XsmyNoFNDua5"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
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      "source": [
        "Prepare Dataset"
      ],
      "metadata": {
        "id": "BiRxRMKODzeg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('insurance.csv')\n",
        "len(dataset)"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "u95d-FigDxci",
        "outputId": "f42e9046-ddd5-41af-e3e6-fedc3a51ab11"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1338"
            ]
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          "metadata": {},
          "execution_count": 4
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      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
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        "id": "qX76bTvpD9MU",
        "outputId": "ec2d036b-ca55-4155-ed94-63ecc4b9a1aa"
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      "execution_count": 5,
      "outputs": [
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              "   age     sex   bmi  children smoker     region  expenses\n",
              "0   19  female  27.9         0    yes  southwest  16884.92\n",
              "1   18    male  33.8         1     no  southeast   1725.55\n",
              "2   28    male  33.0         3     no  southeast   4449.46\n",
              "3   33    male  22.7         0     no  northwest  21984.47\n",
              "4   32    male  28.9         0     no  northwest   3866.86"
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          "metadata": {},
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      "source": [
        "Since the data has some text columns, we need to convert the text values to numeric."
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        "df = dataset\n",
        "df[\"sex\"] = pd.factorize(df[\"sex\"])[0]\n",
        "df[\"region\"] = pd.factorize(df[\"region\"])[0]\n",
        "df[\"smoker\"] = pd.factorize(df[\"smoker\"])[0]\n",
        "dataset = df\n",
        "dataset.head()"
      ],
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        "id": "PouBh9UQEA6S",
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            "text/plain": [
              "   age  sex   bmi  children  smoker  region  expenses\n",
              "0   19    0  27.9         0       0       0  16884.92\n",
              "1   18    1  33.8         1       1       1   1725.55\n",
              "2   28    1  33.0         3       1       1   4449.46\n",
              "3   33    1  22.7         0       1       2  21984.47\n",
              "4   32    1  28.9         0       1       2   3866.86"
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              "      <td>27.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16884.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>33.8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1725.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>33.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4449.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>22.7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>21984.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>28.9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3866.86</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4eb5561-b9ea-481d-930b-9f87879e13d8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a4eb5561-b9ea-481d-930b-9f87879e13d8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a4eb5561-b9ea-481d-930b-9f87879e13d8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9647ffd1-80a0-4324-af71-3d38c72b40b2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9647ffd1-80a0-4324-af71-3d38c72b40b2')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9647ffd1-80a0-4324-af71-3d38c72b40b2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's pick randomly 20% record to make our test_dataset first"
      ],
      "metadata": {
        "id": "0fLp7SJtEH_L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = dataset.sample(frac=0.2)\n",
        "len(test_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fJOZlgbEDdz",
        "outputId": "a0d236b6-6f8b-4c8f-aa38-7fba0b821131"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "268"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we select the remaining 80% to make train_dataset"
      ],
      "metadata": {
        "id": "T6039BkfER_1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = dataset[~dataset.isin(test_dataset)].dropna()\n",
        "len(train_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TDiSPir3EVnh",
        "outputId": "0bb757a4-b901-457e-ff9f-1c3613f2faad"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1070"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LDQ_kt2DEZpj",
        "outputId": "6c9a0018-84b2-4332-dd3b-a26b45d403f9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   bmi  children  smoker  region  expenses\n",
              "0  19.0  0.0  27.9       0.0     0.0     0.0  16884.92\n",
              "2  28.0  1.0  33.0       3.0     1.0     1.0   4449.46\n",
              "3  33.0  1.0  22.7       0.0     1.0     2.0  21984.47\n",
              "4  32.0  1.0  28.9       0.0     1.0     2.0   3866.86\n",
              "5  31.0  0.0  25.7       0.0     1.0     1.0   3756.62"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-180ae721-7d96-4878-9ff1-bd0c19f5fb5d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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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>expenses</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16884.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4449.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21984.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3866.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>31.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3756.62</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-180ae721-7d96-4878-9ff1-bd0c19f5fb5d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-180ae721-7d96-4878-9ff1-bd0c19f5fb5d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-180ae721-7d96-4878-9ff1-bd0c19f5fb5d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-ec25d561-0016-4638-b5b2-e0b86f7b28a2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ec25d561-0016-4638-b5b2-e0b86f7b28a2')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ec25d561-0016-4638-b5b2-e0b86f7b28a2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prepare the labels"
      ],
      "metadata": {
        "id": "so_Jy82dEdff"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels = train_dataset.pop(\"expenses\")\n",
        "train_labels.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aTlBaQhkEf2s",
        "outputId": "137dd248-c012-4e1a-b478-69cb0fe7766e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    16884.92\n",
              "2     4449.46\n",
              "3    21984.47\n",
              "4     3866.86\n",
              "5     3756.62\n",
              "Name: expenses, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2a5hMe6jEjcc",
        "outputId": "90ce974c-295c-458d-ee4c-dfee45e5c356"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   bmi  children  smoker  region\n",
              "0  19.0  0.0  27.9       0.0     0.0     0.0\n",
              "2  28.0  1.0  33.0       3.0     1.0     1.0\n",
              "3  33.0  1.0  22.7       0.0     1.0     2.0\n",
              "4  32.0  1.0  28.9       0.0     1.0     2.0\n",
              "5  31.0  0.0  25.7       0.0     1.0     1.0"
            ],
            "text/html": [
              "\n",
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              "      <th>5</th>\n",
              "      <td>31.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.7</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8af45a1-86ec-4cdf-8dc0-fa51deef0127')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e8af45a1-86ec-4cdf-8dc0-fa51deef0127 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e8af45a1-86ec-4cdf-8dc0-fa51deef0127');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2facfb6a-f390-4bd1-a426-081b56e3d6c7')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2facfb6a-f390-4bd1-a426-081b56e3d6c7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels = test_dataset.pop(\"expenses\")\n",
        "test_labels.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64li5GvtEn9-",
        "outputId": "dc31e613-debc-4ed7-95de-8a0c69e42d61"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "662      5148.55\n",
              "605      9283.56\n",
              "510     11763.00\n",
              "1259    10197.77\n",
              "348      5478.04\n",
              "Name: expenses, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zP3pQxegErRz",
        "outputId": "d281ae02-588a-4bd4-fb81-659e321144dd"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   bmi  children  smoker  region\n",
              "662    32    0  31.5         1       1       3\n",
              "605    51    0  34.1         0       1       1\n",
              "510    56    1  32.1         1       1       3\n",
              "1259   52    0  23.2         0       1       3\n",
              "348    36    0  29.9         1       1       1"
            ],
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <td>51</td>\n",
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              "      <td>32.1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1259</th>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>23.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>348</th>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>29.9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-227bc413-c096-4fe4-9687-d267028ba9c8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-227bc413-c096-4fe4-9687-d267028ba9c8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-227bc413-c096-4fe4-9687-d267028ba9c8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1ca280a5-32a7-4577-ae28-5686f898435b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1ca280a5-32a7-4577-ae28-5686f898435b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1ca280a5-32a7-4577-ae28-5686f898435b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prepare the model"
      ],
      "metadata": {
        "id": "1e4f_g8VEu_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normalizer = layers.experimental.preprocessing.Normalization()\n",
        "normalizer.adapt(np.array(train_dataset))\n",
        "\n",
        "model = keras.Sequential([\n",
        "    normalizer,\n",
        "    layers.Dense(16),\n",
        "    layers.Dense(4),\n",
        "    layers.Dropout(.2),\n",
        "    layers.Dense(1),\n",
        "])"
      ],
      "metadata": {
        "id": "RGegyLLpExFW"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=tf.optimizers.Adam(learning_rate=0.1),\n",
        "    loss='mae',\n",
        "    metrics=['mae', 'mse']\n",
        ")\n",
        "model.build()\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xdy-noMBE1wm",
        "outputId": "97431455-a913-4784-a9ed-b6ad6cdbdcf2"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " normalization (Normalizati  (None, 6)                 13        \n",
            " on)                                                             \n",
            "                                                                 \n",
            " dense (Dense)               (None, 16)                112       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4)                 68        \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 4)                 0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 5         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 198 (796.00 Byte)\n",
            "Trainable params: 185 (740.00 Byte)\n",
            "Non-trainable params: 13 (56.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    train_labels,\n",
        "    epochs=100,\n",
        "    validation_split=0.5,\n",
        "    verbose=0, # disable logging\n",
        ")\n",
        "\n",
        "print(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTLcbAr0E5mf",
        "outputId": "418abb3e-eace-4aec-8027-1c561426d8e0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<keras.src.callbacks.History object at 0x7e27d1d01750>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test"
      ],
      "metadata": {
        "id": "kk3g4l1xE7zh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# RUN THIS CELL TO TEST YOUR MODEL. DO NOT MODIFY CONTENTS.\n",
        "# Test model by checking how well the model generalizes using the test set.\n",
        "loss, mae, mse = model.evaluate(test_dataset, test_labels, verbose=2)\n",
        "\n",
        "print(\"Testing set Mean Abs Error: {:5.2f} expenses\".format(mae))\n",
        "\n",
        "if mae < 3500:\n",
        "  print(\"You passed the challenge. Great job!\")\n",
        "else:\n",
        "  print(\"The Mean Abs Error must be less than 3500. Keep trying.\")\n",
        "\n",
        "# Plot predictions.\n",
        "test_predictions = model.predict(test_dataset).flatten()\n",
        "\n",
        "a = plt.axes(aspect='equal')\n",
        "plt.scatter(test_labels, test_predictions)\n",
        "plt.xlabel('True values (expenses)')\n",
        "plt.ylabel('Predictions (expenses)')\n",
        "lims = [0, 50000]\n",
        "plt.xlim(lims)\n",
        "plt.ylim(lims)\n",
        "_ = plt.plot(lims,lims)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "rSneO6RJE-SG",
        "outputId": "4c176ee6-6d42-432f-a88c-e36ea798101d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 - 0s - loss: 3471.0725 - mae: 3471.0725 - mse: 35882252.0000 - 25ms/epoch - 3ms/step\n",
            "Testing set Mean Abs Error: 3471.07 expenses\n",
            "You passed the challenge. Great job!\n",
            "9/9 [==============================] - 0s 1ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}