fcc_predict_health_costs_with_regression.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hezirel/fcc_ml_health_cost_regression/blob/main/fcc_predict_health_costs_with_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "1rRo8oNqZ-Rj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5004bf4-3328-4bdb-c831-9e785e9cc0b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for tensorflow-docs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n"
          ]
        }
      ],
      "source": [
        "# Import libraries. You may or may not use all of these.\n",
        "!pip install -q git+https://github.com/tensorflow/docs\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "CiX2FI4gZtTt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c54f32ac-fa35-44be-b06d-b162c9c786b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'insurance.csv': No such file or directory\n",
            "--2025-01-04 17:57:51--  https://cdn.freecodecamp.org/project-data/health-costs/insurance.csv\n",
            "Resolving cdn.freecodecamp.org (cdn.freecodecamp.org)... 104.26.2.33, 104.26.3.33, 172.67.70.149, ...\n",
            "Connecting to cdn.freecodecamp.org (cdn.freecodecamp.org)|104.26.2.33|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 50264 (49K) [text/csv]\n",
            "Saving to: ‘insurance.csv’\n",
            "\n",
            "insurance.csv       100%[===================>]  49.09K  --.-KB/s    in 0.007s  \n",
            "\n",
            "2025-01-04 17:57:51 (7.14 MB/s) - ‘insurance.csv’ saved [50264/50264]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Import data\n",
        "!rm insurance.csv\n",
        "!wget https://cdn.freecodecamp.org/project-data/health-costs/insurance.csv\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('insurance.csv')\n",
        "dataset.dropna()"
      ],
      "metadata": {
        "id": "KxywZJFCNa5W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "75c93efa-01e0-4fbc-decb-06a17ecd83b4"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      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\n",
              "...   ...     ...   ...       ...    ...        ...       ...\n",
              "1333   50    male  31.0         3     no  northwest  10600.55\n",
              "1334   18  female  31.9         0     no  northeast   2205.98\n",
              "1335   18  female  36.9         0     no  southeast   1629.83\n",
              "1336   21  female  25.8         0     no  southwest   2007.95\n",
              "1337   61  female  29.1         0    yes  northwest  29141.36\n",
              "\n",
              "[1338 rows x 7 columns]"
            ],
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              "      <td>16884.92</td>\n",
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              "      <th>1</th>\n",
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              "      <td>3</td>\n",
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              "      <td>southeast</td>\n",
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              "      <th>3</th>\n",
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              "      <td>male</td>\n",
              "      <td>22.7</td>\n",
              "      <td>0</td>\n",
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              "      <td>10600.55</td>\n",
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              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
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              "      <td>31.9</td>\n",
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              "      <td>2205.98</td>\n",
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              "      <th>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>36.9</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1629.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>21</td>\n",
              "      <td>female</td>\n",
              "      <td>25.8</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>southwest</td>\n",
              "      <td>2007.95</td>\n",
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              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>29.1</td>\n",
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              "      <td>yes</td>\n",
              "      <td>northwest</td>\n",
              "      <td>29141.36</td>\n",
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              "<p>1338 rows × 7 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 1338,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          21,\n          45,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.098382190003366,\n        \"min\": 16.0,\n        \"max\": 53.1,\n        \"num_unique_values\": 275,\n        \"samples\": [\n          28.6,\n          20.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"southeast\",\n          \"northeast\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"expenses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12110.011239706457,\n        \"min\": 1121.87,\n        \"max\": 63770.43,\n        \"num_unique_values\": 1337,\n        \"samples\": [\n          8688.86,\n          5708.87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical = dataset.select_dtypes(include=['object', 'category']).columns.tolist()\n",
        "numerical = dataset.select_dtypes(include=['int64', 'float64']).columns.tolist()\n",
        "\n",
        "for col in categorical:\n",
        "    dataset[col] = dataset[col].astype('category').cat.codes\n",
        "\n",
        "\n",
        "print(categorical)\n",
        "print(numerical)"
      ],
      "metadata": {
        "id": "bdIuVF2eGXfX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72182b91-c19f-4d01-dfc8-ef691c6eb45e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['sex', 'smoker', 'region']\n",
            "['age', 'bmi', 'children', 'expenses']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_list = ['age', 'sex', 'bmi', 'children', 'smoker', 'region']\n",
        "\n",
        "print(features_list)"
      ],
      "metadata": {
        "id": "3NY2C7KSIBLm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fef02e7-83e1-4ff2-dd39-dfd76546ae5a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['age', 'sex', 'bmi', 'children', 'smoker', 'region']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = dataset.sample(frac=0.8, random_state=0)\n",
        "test_df = dataset.drop(train_df.index)\n",
        "\n",
        "def df_to_ds(src):\n",
        "    features = src.copy()\n",
        "    labels = src.pop('expenses')\n",
        "    return features, labels\n",
        "\n",
        "train_dataset, train_labels = df_to_ds(train_df)\n",
        "test_dataset, test_labels= df_to_ds(test_df)"
      ],
      "metadata": {
        "id": "X8T7Wwl4N8ev"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "normalizer = layers.Normalization(axis=-1)\n",
        "normalizer.adapt(np.array(train_dataset))"
      ],
      "metadata": {
        "id": "oRiVbM4JET04"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    normalizer,\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(32, activation='relu'),\n",
        "    layers.Dense(16, activation='relu'),\n",
        "    layers.Dense(1)\n",
        "    ])\n",
        "\n",
        "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.001),\n",
        "              loss=tf.keras.losses.MeanAbsoluteError(),\n",
        "              metrics=['mae', 'mse'])\n",
        "\n",
        "model.fit(train_dataset, train_labels, epochs=200, verbose=1)"
      ],
      "metadata": {
        "id": "D91BO4xrEUh9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68d78827-23ed-4841-8dec-f88d9447fd12"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 12686.6562 - mae: 12686.6562 - mse: 302805632.0000\n",
            "Epoch 2/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 13115.6904 - mae: 13115.6904 - mse: 316506240.0000 \n",
            "Epoch 3/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 13367.3281 - mae: 13367.3281 - mse: 328772928.0000 \n",
            "Epoch 4/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 13085.9629 - mae: 13085.9629 - mse: 317169216.0000\n",
            "Epoch 5/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 13341.9297 - mae: 13341.9297 - mse: 328022912.0000\n",
            "Epoch 6/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 12516.6562 - mae: 12516.6562 - mse: 293548832.0000\n",
            "Epoch 7/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 12036.7891 - mae: 12036.7891 - mse: 279561920.0000\n",
            "Epoch 8/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 11703.2891 - mae: 11703.2891 - mse: 284802624.0000\n",
            "Epoch 9/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 10645.8301 - mae: 10645.8301 - mse: 239181168.0000\n",
            "Epoch 10/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 9855.4463 - mae: 9855.4463 - mse: 228506720.0000\n",
            "Epoch 11/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 8531.0859 - mae: 8531.0859 - mse: 180484768.0000\n",
            "Epoch 12/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7322.9873 - mae: 7322.9873 - mse: 145079264.0000\n",
            "Epoch 13/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6357.7783 - mae: 6357.7783 - mse: 113325384.0000\n",
            "Epoch 14/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4997.1353 - mae: 4997.1353 - mse: 76662160.0000\n",
            "Epoch 15/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 3836.2764 - mae: 3836.2764 - mse: 50651408.0000\n",
            "Epoch 16/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 2925.3169 - mae: 2925.3169 - mse: 30920062.0000\n",
            "Epoch 17/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 2456.2063 - mae: 2456.2063 - mse: 21117560.0000\n",
            "Epoch 18/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 2261.8572 - mae: 2261.8572 - mse: 15970816.0000\n",
            "Epoch 19/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1912.8501 - mae: 1912.8501 - mse: 11393028.0000\n",
            "Epoch 20/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1666.5852 - mae: 1666.5852 - mse: 9256851.0000   \n",
            "Epoch 21/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1611.2769 - mae: 1611.2769 - mse: 8666922.0000  \n",
            "Epoch 22/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1509.5741 - mae: 1509.5741 - mse: 7765814.5000 \n",
            "Epoch 23/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1440.6234 - mae: 1440.6234 - mse: 7597808.0000 \n",
            "Epoch 24/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1363.4496 - mae: 1363.4496 - mse: 7029217.5000 \n",
            "Epoch 25/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1303.1073 - mae: 1303.1073 - mse: 6613537.5000\n",
            "Epoch 26/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1316.5116 - mae: 1316.5116 - mse: 6842712.0000 \n",
            "Epoch 27/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1293.5248 - mae: 1293.5248 - mse: 6988591.5000 \n",
            "Epoch 28/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1151.2109 - mae: 1151.2109 - mse: 5307547.5000\n",
            "Epoch 29/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1286.5521 - mae: 1286.5521 - mse: 6869858.0000\n",
            "Epoch 30/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1099.1078 - mae: 1099.1078 - mse: 5307141.0000 \n",
            "Epoch 31/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1147.7589 - mae: 1147.7589 - mse: 6012810.0000  \n",
            "Epoch 32/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1038.7699 - mae: 1038.7699 - mse: 4894420.5000\n",
            "Epoch 33/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 1034.3684 - mae: 1034.3684 - mse: 4708033.5000 \n",
            "Epoch 34/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 863.3360 - mae: 863.3360 - mse: 3432555.7500 \n",
            "Epoch 35/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 873.5805 - mae: 873.5805 - mse: 3666703.5000\n",
            "Epoch 36/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 857.6423 - mae: 857.6423 - mse: 3576580.2500 \n",
            "Epoch 37/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 807.4213 - mae: 807.4213 - mse: 3102089.7500 \n",
            "Epoch 38/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 766.4709 - mae: 766.4709 - mse: 3046904.5000 \n",
            "Epoch 39/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 778.7263 - mae: 778.7263 - mse: 3111232.7500   \n",
            "Epoch 40/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 639.3784 - mae: 639.3784 - mse: 2161777.5000\n",
            "Epoch 41/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 680.8889 - mae: 680.8889 - mse: 2306576.2500\n",
            "Epoch 42/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 682.6964 - mae: 682.6964 - mse: 2468572.0000 \n",
            "Epoch 43/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 612.6743 - mae: 612.6743 - mse: 2111285.0000\n",
            "Epoch 44/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 614.2776 - mae: 614.2776 - mse: 2081748.1250 \n",
            "Epoch 45/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 635.2440 - mae: 635.2440 - mse: 2079580.6250 \n",
            "Epoch 46/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 536.6565 - mae: 536.6565 - mse: 1565990.2500\n",
            "Epoch 47/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 481.9106 - mae: 481.9106 - mse: 1351591.7500 \n",
            "Epoch 48/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 527.8865 - mae: 527.8865 - mse: 1569984.2500\n",
            "Epoch 49/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 456.4354 - mae: 456.4354 - mse: 1234220.3750\n",
            "Epoch 50/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 418.7780 - mae: 418.7780 - mse: 996344.4375 \n",
            "Epoch 51/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 430.8485 - mae: 430.8485 - mse: 1146763.6250 \n",
            "Epoch 52/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 367.2216 - mae: 367.2216 - mse: 851234.3750\n",
            "Epoch 53/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 408.6385 - mae: 408.6385 - mse: 947270.3750\n",
            "Epoch 54/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 345.1802 - mae: 345.1802 - mse: 780235.1250\n",
            "Epoch 55/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 369.1978 - mae: 369.1978 - mse: 867281.8750\n",
            "Epoch 56/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 310.5848 - mae: 310.5848 - mse: 616472.8750\n",
            "Epoch 57/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 307.7843 - mae: 307.7843 - mse: 602355.6875\n",
            "Epoch 58/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 291.3260 - mae: 291.3260 - mse: 590113.7500\n",
            "Epoch 59/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 295.0238 - mae: 295.0238 - mse: 604345.5000\n",
            "Epoch 60/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 291.8937 - mae: 291.8937 - mse: 567025.5000\n",
            "Epoch 61/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 240.9103 - mae: 240.9103 - mse: 388010.6250\n",
            "Epoch 62/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 264.5870 - mae: 264.5870 - mse: 475664.8750\n",
            "Epoch 63/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 240.7300 - mae: 240.7300 - mse: 387096.1875\n",
            "Epoch 64/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 257.3195 - mae: 257.3195 - mse: 444346.8125\n",
            "Epoch 65/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 231.9364 - mae: 231.9364 - mse: 361599.6562\n",
            "Epoch 66/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 246.2312 - mae: 246.2312 - mse: 424686.9062\n",
            "Epoch 67/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 231.6252 - mae: 231.6252 - mse: 357528.1875\n",
            "Epoch 68/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 219.8812 - mae: 219.8812 - mse: 334817.1875\n",
            "Epoch 69/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 206.5166 - mae: 206.5166 - mse: 292699.6250\n",
            "Epoch 70/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 198.8390 - mae: 198.8390 - mse: 262159.1875\n",
            "Epoch 71/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 192.1198 - mae: 192.1198 - mse: 251335.5000\n",
            "Epoch 72/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 191.2155 - mae: 191.2155 - mse: 262995.0000\n",
            "Epoch 73/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 215.9047 - mae: 215.9047 - mse: 341329.9688\n",
            "Epoch 74/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 214.6613 - mae: 214.6613 - mse: 301751.5938\n",
            "Epoch 75/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 166.8513 - mae: 166.8513 - mse: 190286.2031\n",
            "Epoch 76/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 184.2105 - mae: 184.2105 - mse: 245900.2500\n",
            "Epoch 77/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 191.9397 - mae: 191.9397 - mse: 260012.6875\n",
            "Epoch 78/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 171.7459 - mae: 171.7459 - mse: 230393.7344\n",
            "Epoch 79/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 188.4033 - mae: 188.4033 - mse: 248908.8125\n",
            "Epoch 80/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 192.3233 - mae: 192.3233 - mse: 268266.4375\n",
            "Epoch 81/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 181.7187 - mae: 181.7187 - mse: 255993.9062\n",
            "Epoch 82/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 182.7556 - mae: 182.7556 - mse: 224783.0938\n",
            "Epoch 83/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 184.9637 - mae: 184.9637 - mse: 246691.7656\n",
            "Epoch 84/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 189.5756 - mae: 189.5756 - mse: 263867.7812\n",
            "Epoch 85/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 169.7081 - mae: 169.7081 - mse: 226458.6875\n",
            "Epoch 86/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 192.4201 - mae: 192.4201 - mse: 284000.9688\n",
            "Epoch 87/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 166.3406 - mae: 166.3406 - mse: 217445.7500\n",
            "Epoch 88/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 169.5424 - mae: 169.5424 - mse: 216090.2031\n",
            "Epoch 89/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 182.2853 - mae: 182.2853 - mse: 262778.8438\n",
            "Epoch 90/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 171.9037 - mae: 171.9037 - mse: 239082.3906\n",
            "Epoch 91/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 153.3558 - mae: 153.3558 - mse: 187543.0000\n",
            "Epoch 92/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 156.9810 - mae: 156.9810 - mse: 207551.6250\n",
            "Epoch 93/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 136.1673 - mae: 136.1673 - mse: 163589.2656\n",
            "Epoch 94/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 164.4012 - mae: 164.4012 - mse: 228780.3281\n",
            "Epoch 95/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 130.3537 - mae: 130.3537 - mse: 148281.2031\n",
            "Epoch 96/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 178.8698 - mae: 178.8698 - mse: 279277.1875\n",
            "Epoch 97/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 143.7856 - mae: 143.7856 - mse: 196206.3438 \n",
            "Epoch 98/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 129.6584 - mae: 129.6584 - mse: 163377.0781\n",
            "Epoch 99/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 133.8429 - mae: 133.8429 - mse: 160108.6250\n",
            "Epoch 100/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 150.9756 - mae: 150.9756 - mse: 207534.7344 \n",
            "Epoch 101/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 134.2546 - mae: 134.2546 - mse: 168912.3594 \n",
            "Epoch 102/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 155.9169 - mae: 155.9169 - mse: 196410.7656\n",
            "Epoch 103/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 126.5693 - mae: 126.5693 - mse: 150605.9688\n",
            "Epoch 104/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 149.1460 - mae: 149.1460 - mse: 201464.9844 \n",
            "Epoch 105/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 127.1389 - mae: 127.1389 - mse: 139656.6250\n",
            "Epoch 106/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 144.5922 - mae: 144.5922 - mse: 188682.6875 \n",
            "Epoch 107/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 134.6436 - mae: 134.6436 - mse: 158556.9375\n",
            "Epoch 108/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 132.7094 - mae: 132.7094 - mse: 158590.8125\n",
            "Epoch 109/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 123.4916 - mae: 123.4916 - mse: 148493.0312 \n",
            "Epoch 110/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 121.9408 - mae: 121.9408 - mse: 144120.9219\n",
            "Epoch 111/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 111.8197 - mae: 111.8197 - mse: 120714.7812\n",
            "Epoch 112/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 131.6526 - mae: 131.6526 - mse: 173915.0781\n",
            "Epoch 113/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 127.3502 - mae: 127.3502 - mse: 155377.5938\n",
            "Epoch 114/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 120.7289 - mae: 120.7289 - mse: 139312.3750 \n",
            "Epoch 115/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 127.4449 - mae: 127.4449 - mse: 147608.0469\n",
            "Epoch 116/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 120.1265 - mae: 120.1265 - mse: 146600.9688\n",
            "Epoch 117/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 109.6431 - mae: 109.6431 - mse: 117361.7266\n",
            "Epoch 118/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 130.6950 - mae: 130.6950 - mse: 154099.6719 \n",
            "Epoch 119/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 126.8360 - mae: 126.8360 - mse: 144734.0938 \n",
            "Epoch 120/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 116.7231 - mae: 116.7231 - mse: 131589.3438\n",
            "Epoch 121/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 119.5974 - mae: 119.5974 - mse: 128480.1875\n",
            "Epoch 122/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 117.5758 - mae: 117.5758 - mse: 132072.2500 \n",
            "Epoch 123/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 104.4107 - mae: 104.4107 - mse: 105934.1094\n",
            "Epoch 124/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 123.7086 - mae: 123.7086 - mse: 139154.0000\n",
            "Epoch 125/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 125.5785 - mae: 125.5785 - mse: 140562.5156\n",
            "Epoch 126/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 122.8423 - mae: 122.8423 - mse: 140210.5781 \n",
            "Epoch 127/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 111.9625 - mae: 111.9625 - mse: 126595.6641 \n",
            "Epoch 128/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 119.5985 - mae: 119.5985 - mse: 135516.0938 \n",
            "Epoch 129/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 116.3639 - mae: 116.3639 - mse: 136000.3594 \n",
            "Epoch 130/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 112.4435 - mae: 112.4435 - mse: 125585.1641\n",
            "Epoch 131/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 94.8057 - mae: 94.8057 - mse: 86011.3750\n",
            "Epoch 132/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 116.7298 - mae: 116.7298 - mse: 132852.4688\n",
            "Epoch 133/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 107.5688 - mae: 107.5688 - mse: 115860.9844\n",
            "Epoch 134/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 114.1307 - mae: 114.1307 - mse: 123615.3828\n",
            "Epoch 135/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 115.7571 - mae: 115.7571 - mse: 125454.0156\n",
            "Epoch 136/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 102.0955 - mae: 102.0955 - mse: 99617.0000  \n",
            "Epoch 137/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 113.9984 - mae: 113.9984 - mse: 123625.8750 \n",
            "Epoch 138/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 115.8751 - mae: 115.8751 - mse: 118466.4609\n",
            "Epoch 139/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 112.7306 - mae: 112.7306 - mse: 110370.5391\n",
            "Epoch 140/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 116.6805 - mae: 116.6805 - mse: 131532.1094 \n",
            "Epoch 141/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 104.9176 - mae: 104.9176 - mse: 104577.0469\n",
            "Epoch 142/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 103.2846 - mae: 103.2846 - mse: 98524.1797\n",
            "Epoch 143/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 109.0852 - mae: 109.0852 - mse: 108892.2578\n",
            "Epoch 144/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 113.9703 - mae: 113.9703 - mse: 110471.6484 \n",
            "Epoch 145/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 106.9465 - mae: 106.9465 - mse: 119983.4609 \n",
            "Epoch 146/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 95.1644 - mae: 95.1644 - mse: 85255.3203 \n",
            "Epoch 147/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 102.8541 - mae: 102.8541 - mse: 107004.5078\n",
            "Epoch 148/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 115.6602 - mae: 115.6602 - mse: 129569.4922 \n",
            "Epoch 149/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 97.7258 - mae: 97.7258 - mse: 89990.6406   \n",
            "Epoch 150/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 102.2847 - mae: 102.2847 - mse: 98128.8047\n",
            "Epoch 151/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 101.6109 - mae: 101.6109 - mse: 94559.8125  \n",
            "Epoch 152/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 113.2764 - mae: 113.2764 - mse: 116190.9453 \n",
            "Epoch 153/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 109.4727 - mae: 109.4727 - mse: 98434.1094  \n",
            "Epoch 154/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 110.8295 - mae: 110.8295 - mse: 110653.3047\n",
            "Epoch 155/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 98.4891 - mae: 98.4891 - mse: 97059.0781\n",
            "Epoch 156/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 98.5472 - mae: 98.5472 - mse: 92026.2109    \n",
            "Epoch 157/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 102.4931 - mae: 102.4931 - mse: 110575.2188\n",
            "Epoch 158/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 96.3877 - mae: 96.3877 - mse: 90360.4844    \n",
            "Epoch 159/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 105.4258 - mae: 105.4258 - mse: 109537.2188\n",
            "Epoch 160/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 110.8376 - mae: 110.8376 - mse: 109478.4141 \n",
            "Epoch 161/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 93.4728 - mae: 93.4728 - mse: 91688.2500\n",
            "Epoch 162/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 95.5128 - mae: 95.5128 - mse: 86727.8594\n",
            "Epoch 163/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 98.8910 - mae: 98.8910 - mse: 88963.7891   \n",
            "Epoch 164/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 94.8859 - mae: 94.8859 - mse: 80911.4531\n",
            "Epoch 165/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 94.1112 - mae: 94.1112 - mse: 86776.2969\n",
            "Epoch 166/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 97.6539 - mae: 97.6539 - mse: 92380.6875    \n",
            "Epoch 167/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 94.5077 - mae: 94.5077 - mse: 84035.9531 \n",
            "Epoch 168/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 98.6755 - mae: 98.6755 - mse: 89459.0000 \n",
            "Epoch 169/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 88.7762 - mae: 88.7762 - mse: 77924.7266 \n",
            "Epoch 170/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 109.3420 - mae: 109.3420 - mse: 97951.3516\n",
            "Epoch 171/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 84.4181 - mae: 84.4181 - mse: 73476.9453    \n",
            "Epoch 172/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 89.6445 - mae: 89.6445 - mse: 80247.9141   \n",
            "Epoch 173/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 94.1517 - mae: 94.1517 - mse: 94456.1719 \n",
            "Epoch 174/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 97.0404 - mae: 97.0404 - mse: 85466.5938 \n",
            "Epoch 175/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 86.4616 - mae: 86.4616 - mse: 79712.8359 \n",
            "Epoch 176/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 108.9679 - mae: 108.9679 - mse: 101457.7344\n",
            "Epoch 177/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 103.4690 - mae: 103.4690 - mse: 92131.0000\n",
            "Epoch 178/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 89.9504 - mae: 89.9504 - mse: 83897.0781\n",
            "Epoch 179/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 85.1186 - mae: 85.1186 - mse: 77226.2969\n",
            "Epoch 180/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 86.6016 - mae: 86.6016 - mse: 74151.7656\n",
            "Epoch 181/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 81.8827 - mae: 81.8827 - mse: 74182.0469\n",
            "Epoch 182/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 89.8140 - mae: 89.8140 - mse: 95486.8203 \n",
            "Epoch 183/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 78.2389 - mae: 78.2389 - mse: 69823.3750\n",
            "Epoch 184/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 84.8653 - mae: 84.8653 - mse: 77288.6562\n",
            "Epoch 185/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 76.0208 - mae: 76.0208 - mse: 58529.1484\n",
            "Epoch 186/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 82.9907 - mae: 82.9907 - mse: 72384.3281\n",
            "Epoch 187/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 86.0137 - mae: 86.0137 - mse: 80018.8047\n",
            "Epoch 188/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 82.5953 - mae: 82.5953 - mse: 81830.0078\n",
            "Epoch 189/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 85.5953 - mae: 85.5953 - mse: 69926.0781\n",
            "Epoch 190/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 95.1486 - mae: 95.1486 - mse: 99871.7969 \n",
            "Epoch 191/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 84.7784 - mae: 84.7784 - mse: 69635.8359\n",
            "Epoch 192/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 83.3654 - mae: 83.3654 - mse: 68812.8516\n",
            "Epoch 193/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 87.3058 - mae: 87.3058 - mse: 75653.6641\n",
            "Epoch 194/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 88.9922 - mae: 88.9922 - mse: 69888.6484    \n",
            "Epoch 195/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 82.0561 - mae: 82.0561 - mse: 66846.6406 \n",
            "Epoch 196/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 86.5639 - mae: 86.5639 - mse: 82900.6719    \n",
            "Epoch 197/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 75.5791 - mae: 75.5791 - mse: 60569.5312\n",
            "Epoch 198/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 76.2606 - mae: 76.2606 - mse: 60866.8984   \n",
            "Epoch 199/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 77.1895 - mae: 77.1895 - mse: 55073.0273\n",
            "Epoch 200/200\n",
            "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 77.5792 - mae: 77.5792 - mse: 68465.6094 \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7824d6b20070>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Xe7RXH3N3CWU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "a8c6163d-c45a-461d-eb45-4b978b023eb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 - 0s - 17ms/step - loss: 112.7463 - mae: 112.7463 - mse: 109257.4219\n",
            "Testing set Mean Abs Error: 112.75 expenses\n",
            "You passed the challenge. Great job!\n",
            "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "fcc_predict_health_costs_with_regression.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}