medical_cost.ipynb

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        "<a href=\"https://colab.research.google.com/github/sasumwen/medical-cost/blob/main/medical_cost.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import tensorflow as tf\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
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    {
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      "source": [
        "# Read in the insurance dataset\n",
        "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")\n",
        "insurance"
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              "      age     sex     bmi  children smoker     region      charges\n",
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              "1336   21  female  25.800         0     no  southwest   2007.94500\n",
              "1337   61  female  29.070         0    yes  northwest  29141.36030\n",
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      "source": [
        "insurance.info()"
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      "execution_count": 22,
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1338 non-null   int64  \n",
            " 1   sex       1338 non-null   object \n",
            " 2   bmi       1338 non-null   float64\n",
            " 3   children  1338 non-null   int64  \n",
            " 4   smoker    1338 non-null   object \n",
            " 5   region    1338 non-null   object \n",
            " 6   charges   1338 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.3+ KB\n"
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    {
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      "source": [
        "# one hot encoding the categorical features\n",
        "\n",
        "insurance_one_hoted = pd.get_dummies(insurance)\n",
        "insurance_one_hoted"
      ],
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      "execution_count": 23,
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              "      age     bmi  children      charges  sex_female  sex_male  smoker_no  \\\n",
              "0      19  27.900         0  16884.92400           1         0          0   \n",
              "1      18  33.770         1   1725.55230           0         1          1   \n",
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              "1337   61  29.070         0  29141.36030           1         0          0   \n",
              "\n",
              "      smoker_yes  region_northeast  region_northwest  region_southeast  \\\n",
              "0              1                 0                 0                 0   \n",
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              "2              0                 0                 0                 1   \n",
              "3              0                 0                 1                 0   \n",
              "4              0                 0                 1                 0   \n",
              "...          ...               ...               ...               ...   \n",
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              "1335           0                 0                 0                 1   \n",
              "1336           0                 0                 0                 0   \n",
              "1337           1                 0                 1                 0   \n",
              "\n",
              "      region_southwest  \n",
              "0                    1  \n",
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              "2                    0  \n",
              "3                    0  \n",
              "4                    0  \n",
              "...                ...  \n",
              "1333                 0  \n",
              "1334                 0  \n",
              "1335                 0  \n",
              "1336                 1  \n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>1629.83350</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>21</td>\n",
              "      <td>25.800</td>\n",
              "      <td>0</td>\n",
              "      <td>2007.94500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>29.070</td>\n",
              "      <td>0</td>\n",
              "      <td>29141.36030</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80f47d9d-652c-4f50-99fb-4bf8008c3a61')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-80f47d9d-652c-4f50-99fb-4bf8008c3a61 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-80f47d9d-652c-4f50-99fb-4bf8008c3a61');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create features and labels\n",
        "\n",
        "X = insurance_one_hoted.drop('charges', axis =1)\n",
        "y = insurance_one_hoted[\"charges\"]\n",
        "\n",
        "X.head(), y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o-Ii7Vts5l9Z",
        "outputId": "e69955c0-348b-437e-95cb-674f6184a74d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(   age     bmi  children  sex_female  sex_male  smoker_no  smoker_yes  \\\n",
              " 0   19  27.900         0           1         0          0           1   \n",
              " 1   18  33.770         1           0         1          1           0   \n",
              " 2   28  33.000         3           0         1          1           0   \n",
              " 3   33  22.705         0           0         1          1           0   \n",
              " 4   32  28.880         0           0         1          1           0   \n",
              " \n",
              "    region_northeast  region_northwest  region_southeast  region_southwest  \n",
              " 0                 0                 0                 0                 1  \n",
              " 1                 0                 0                 1                 0  \n",
              " 2                 0                 0                 1                 0  \n",
              " 3                 0                 1                 0                 0  \n",
              " 4                 0                 1                 0                 0  ,\n",
              " 0    16884.92400\n",
              " 1     1725.55230\n",
              " 2     4449.46200\n",
              " 3    21984.47061\n",
              " 4     3866.85520\n",
              " Name: charges, dtype: float64)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create train and test set\n",
        "tf.random.set_seed(84)\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, \n",
        "                                                    y,\n",
        "                                                    test_size=0.2)\n",
        "\n",
        "len(X), len(X_train), len(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bdekgd1O6pMR",
        "outputId": "842632e5-f58e-4204-ee3f-4e152da58bc1"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1338, 1070, 268)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building the model\n",
        "tf.random.set_seed(84)\n",
        "\n",
        "insurance_model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100, activation = \"relu\"),\n",
        "    tf.keras.layers.Dense(100, activation = \"relu\"),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# compile\n",
        "insurance_model.compile(loss=tf.keras.losses.mae,\n",
        "                        optimizer = tf.keras.optimizers.SGD(),\n",
        "                        metrics = [\"mae\"]\n",
        "                        )\n",
        "\n",
        "# fit\n",
        "insurance_model.fit(tf.expand_dims(X_train, -1), y_train, epochs = 500)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNJQi7-Y7f4e",
        "outputId": "0cd8faad-2009-47ef-a527-b559ac6e9a3d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "34/34 [==============================] - 1s 4ms/step - loss: 12612.8223 - mae: 12612.8223\n",
            "Epoch 2/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 10839.8945 - mae: 10839.8945\n",
            "Epoch 3/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8638.1963 - mae: 8638.1963\n",
            "Epoch 4/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 9074.8398 - mae: 9074.8398\n",
            "Epoch 5/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8701.8555 - mae: 8701.8555\n",
            "Epoch 6/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8597.0508 - mae: 8597.0508\n",
            "Epoch 7/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8484.2988 - mae: 8484.2988\n",
            "Epoch 8/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8620.2637 - mae: 8620.2637\n",
            "Epoch 9/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8546.4248 - mae: 8546.4248\n",
            "Epoch 10/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8541.3643 - mae: 8541.3643\n",
            "Epoch 11/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8450.2041 - mae: 8450.2041\n",
            "Epoch 12/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8442.3867 - mae: 8442.3867\n",
            "Epoch 13/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8537.8271 - mae: 8537.8271\n",
            "Epoch 14/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8542.2939 - mae: 8542.2939\n",
            "Epoch 15/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8579.9482 - mae: 8579.9482\n",
            "Epoch 16/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8533.1064 - mae: 8533.1064\n",
            "Epoch 17/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8487.2314 - mae: 8487.2314\n",
            "Epoch 18/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8513.6699 - mae: 8513.6699\n",
            "Epoch 19/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8669.5635 - mae: 8669.5635\n",
            "Epoch 20/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8708.3584 - mae: 8708.3584\n",
            "Epoch 21/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8578.1631 - mae: 8578.1631\n",
            "Epoch 22/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8515.6514 - mae: 8515.6514\n",
            "Epoch 23/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8510.3545 - mae: 8510.3545\n",
            "Epoch 24/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8525.9111 - mae: 8525.9111\n",
            "Epoch 25/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8642.6670 - mae: 8642.6670\n",
            "Epoch 26/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8572.2305 - mae: 8572.2305\n",
            "Epoch 27/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8608.7100 - mae: 8608.7100\n",
            "Epoch 28/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8539.5508 - mae: 8539.5508\n",
            "Epoch 29/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8477.2383 - mae: 8477.2383\n",
            "Epoch 30/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8653.9404 - mae: 8653.9404\n",
            "Epoch 31/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8522.5117 - mae: 8522.5117\n",
            "Epoch 32/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8572.9014 - mae: 8572.9014\n",
            "Epoch 33/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8512.9697 - mae: 8512.9697\n",
            "Epoch 34/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8533.1826 - mae: 8533.1826\n",
            "Epoch 35/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8522.7676 - mae: 8522.7676\n",
            "Epoch 36/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8583.1729 - mae: 8583.1729\n",
            "Epoch 37/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8495.5654 - mae: 8495.5654\n",
            "Epoch 38/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8431.9961 - mae: 8431.9961\n",
            "Epoch 39/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8566.3057 - mae: 8566.3057\n",
            "Epoch 40/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8555.7988 - mae: 8555.7988\n",
            "Epoch 41/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8429.6660 - mae: 8429.6660\n",
            "Epoch 42/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8506.6787 - mae: 8506.6787\n",
            "Epoch 43/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8540.7695 - mae: 8540.7695\n",
            "Epoch 44/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8489.0205 - mae: 8489.0205\n",
            "Epoch 45/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8477.5332 - mae: 8477.5332\n",
            "Epoch 46/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8576.9238 - mae: 8576.9238\n",
            "Epoch 47/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8673.8584 - mae: 8673.8584\n",
            "Epoch 48/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8525.3135 - mae: 8525.3135\n",
            "Epoch 49/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8544.1348 - mae: 8544.1348\n",
            "Epoch 50/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8459.3896 - mae: 8459.3896\n",
            "Epoch 51/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8429.1787 - mae: 8429.1787\n",
            "Epoch 52/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8529.4541 - mae: 8529.4541\n",
            "Epoch 53/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8515.6797 - mae: 8515.6797\n",
            "Epoch 54/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8503.2930 - mae: 8503.2930\n",
            "Epoch 55/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8419.5771 - mae: 8419.5771\n",
            "Epoch 56/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8501.5361 - mae: 8501.5361\n",
            "Epoch 57/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8404.1895 - mae: 8404.1895\n",
            "Epoch 58/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8478.4746 - mae: 8478.4746\n",
            "Epoch 59/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8522.6826 - mae: 8522.6826\n",
            "Epoch 60/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8439.1787 - mae: 8439.1787\n",
            "Epoch 61/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8439.6455 - mae: 8439.6455\n",
            "Epoch 62/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8541.0918 - mae: 8541.0918\n",
            "Epoch 63/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8589.8213 - mae: 8589.8213\n",
            "Epoch 64/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8506.0342 - mae: 8506.0342\n",
            "Epoch 65/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8467.1924 - mae: 8467.1924\n",
            "Epoch 66/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8364.0674 - mae: 8364.0674\n",
            "Epoch 67/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8489.0234 - mae: 8489.0234\n",
            "Epoch 68/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8515.1768 - mae: 8515.1768\n",
            "Epoch 69/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8395.5107 - mae: 8395.5107\n",
            "Epoch 70/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8467.7900 - mae: 8467.7900\n",
            "Epoch 71/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8473.5527 - mae: 8473.5527\n",
            "Epoch 72/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8410.8701 - mae: 8410.8701\n",
            "Epoch 73/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8444.9375 - mae: 8444.9375\n",
            "Epoch 74/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8403.2930 - mae: 8403.2930\n",
            "Epoch 75/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8453.4990 - mae: 8453.4990\n",
            "Epoch 76/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8435.1631 - mae: 8435.1631\n",
            "Epoch 77/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8476.9453 - mae: 8476.9453\n",
            "Epoch 78/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8456.8027 - mae: 8456.8027\n",
            "Epoch 79/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8448.6836 - mae: 8448.6836\n",
            "Epoch 80/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8437.7832 - mae: 8437.7832\n",
            "Epoch 81/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8425.5020 - mae: 8425.5020\n",
            "Epoch 82/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8435.0791 - mae: 8435.0791\n",
            "Epoch 83/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8467.2588 - mae: 8467.2588\n",
            "Epoch 84/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8396.6406 - mae: 8396.6406\n",
            "Epoch 85/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8440.2900 - mae: 8440.2900\n",
            "Epoch 86/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8396.0469 - mae: 8396.0469\n",
            "Epoch 87/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8459.4688 - mae: 8459.4688\n",
            "Epoch 88/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8409.7285 - mae: 8409.7285\n",
            "Epoch 89/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8400.0674 - mae: 8400.0674\n",
            "Epoch 90/500\n",
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            "34/34 [==============================] - 0s 4ms/step - loss: 8382.1719 - mae: 8382.1719\n",
            "Epoch 422/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8434.2480 - mae: 8434.2480\n",
            "Epoch 423/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8383.7695 - mae: 8383.7695\n",
            "Epoch 424/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8362.1064 - mae: 8362.1064\n",
            "Epoch 425/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8415.6592 - mae: 8415.6592\n",
            "Epoch 426/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8362.9229 - mae: 8362.9229\n",
            "Epoch 427/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8400.1709 - mae: 8400.1709\n",
            "Epoch 428/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8367.2080 - mae: 8367.2080\n",
            "Epoch 429/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8380.9756 - mae: 8380.9756\n",
            "Epoch 430/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8395.4424 - mae: 8395.4424\n",
            "Epoch 431/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8449.2949 - mae: 8449.2949\n",
            "Epoch 432/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8374.2041 - mae: 8374.2041\n",
            "Epoch 433/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8383.2500 - mae: 8383.2500\n",
            "Epoch 434/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8405.7627 - mae: 8405.7627\n",
            "Epoch 435/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8416.2139 - mae: 8416.2139\n",
            "Epoch 436/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8394.6348 - mae: 8394.6348\n",
            "Epoch 437/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8394.1133 - mae: 8394.1133\n",
            "Epoch 438/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8401.9697 - mae: 8401.9697\n",
            "Epoch 439/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8417.2793 - mae: 8417.2793\n",
            "Epoch 440/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8390.9785 - mae: 8390.9785\n",
            "Epoch 441/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8403.6094 - mae: 8403.6094\n",
            "Epoch 442/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8398.3027 - mae: 8398.3027\n",
            "Epoch 443/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8383.6445 - mae: 8383.6445\n",
            "Epoch 444/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8437.4727 - mae: 8437.4727\n",
            "Epoch 445/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8396.1582 - mae: 8396.1582\n",
            "Epoch 446/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8400.9160 - mae: 8400.9160\n",
            "Epoch 447/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8387.1709 - mae: 8387.1709\n",
            "Epoch 448/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8372.4854 - mae: 8372.4854\n",
            "Epoch 449/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8406.2832 - mae: 8406.2832\n",
            "Epoch 450/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8408.7227 - mae: 8408.7227\n",
            "Epoch 451/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8413.5020 - mae: 8413.5020\n",
            "Epoch 452/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8416.4434 - mae: 8416.4434\n",
            "Epoch 453/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8338.9521 - mae: 8338.9521\n",
            "Epoch 454/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8376.0029 - mae: 8376.0029\n",
            "Epoch 455/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8364.3535 - mae: 8364.3535\n",
            "Epoch 456/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8413.5371 - mae: 8413.5371\n",
            "Epoch 457/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8411.7861 - mae: 8411.7861\n",
            "Epoch 458/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8372.7402 - mae: 8372.7402\n",
            "Epoch 459/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8387.4023 - mae: 8387.4023\n",
            "Epoch 460/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8443.0166 - mae: 8443.0166\n",
            "Epoch 461/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8409.1484 - mae: 8409.1484\n",
            "Epoch 462/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8402.0420 - mae: 8402.0420\n",
            "Epoch 463/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8345.2881 - mae: 8345.2881\n",
            "Epoch 464/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8387.0234 - mae: 8387.0234\n",
            "Epoch 465/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8342.4600 - mae: 8342.4600\n",
            "Epoch 466/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8398.7500 - mae: 8398.7500\n",
            "Epoch 467/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8388.1338 - mae: 8388.1338\n",
            "Epoch 468/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8402.7285 - mae: 8402.7285\n",
            "Epoch 469/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8365.7100 - mae: 8365.7100\n",
            "Epoch 470/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8416.2158 - mae: 8416.2158\n",
            "Epoch 471/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8446.7783 - mae: 8446.7783\n",
            "Epoch 472/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8378.2051 - mae: 8378.2051\n",
            "Epoch 473/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8362.3125 - mae: 8362.3125\n",
            "Epoch 474/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8373.0869 - mae: 8373.0869\n",
            "Epoch 475/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8365.9961 - mae: 8365.9961\n",
            "Epoch 476/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8441.7949 - mae: 8441.7949\n",
            "Epoch 477/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8396.6748 - mae: 8396.6748\n",
            "Epoch 478/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8375.8242 - mae: 8375.8242\n",
            "Epoch 479/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8413.2725 - mae: 8413.2725\n",
            "Epoch 480/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8387.0586 - mae: 8387.0586\n",
            "Epoch 481/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8360.8545 - mae: 8360.8545\n",
            "Epoch 482/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8382.3604 - mae: 8382.3604\n",
            "Epoch 483/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8393.7236 - mae: 8393.7236\n",
            "Epoch 484/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8379.3369 - mae: 8379.3369\n",
            "Epoch 485/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8421.0449 - mae: 8421.0449\n",
            "Epoch 486/500\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 8406.9502 - mae: 8406.9502\n",
            "Epoch 487/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8409.1338 - mae: 8409.1338\n",
            "Epoch 488/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8409.3379 - mae: 8409.3379\n",
            "Epoch 489/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8400.6533 - mae: 8400.6533\n",
            "Epoch 490/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8412.3066 - mae: 8412.3066\n",
            "Epoch 491/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8402.5166 - mae: 8402.5166\n",
            "Epoch 492/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8394.2900 - mae: 8394.2900\n",
            "Epoch 493/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8379.2119 - mae: 8379.2119\n",
            "Epoch 494/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8430.1074 - mae: 8430.1074\n",
            "Epoch 495/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8356.3047 - mae: 8356.3047\n",
            "Epoch 496/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8393.0586 - mae: 8393.0586\n",
            "Epoch 497/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8374.2090 - mae: 8374.2090\n",
            "Epoch 498/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8367.7158 - mae: 8367.7158\n",
            "Epoch 499/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8372.0244 - mae: 8372.0244\n",
            "Epoch 500/500\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 8386.2559 - mae: 8386.2559\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc404762b50>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check the result of the insurance on the test data by evaluating\n",
        "\n",
        "insurance_model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "id": "_JQrd3m_9TWz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44bac5f9-44fe-48fb-ae8f-8834bee53fc1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step - loss: 8200.8340 - mae: 8200.8340\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[8200.833984375, 8200.833984375]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Being wrong by over 8000 shows the model isn't performing well., lets try improvement"
      ],
      "metadata": {
        "id": "eOxyteBWrNy7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed\n",
        "callback= tf.keras.callbacks.EarlyStopping(monitor = \"mae\", patience = 3)\n",
        "\n",
        "insurance_model2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(570),\n",
        "    tf.keras.layers.Dense(1),\n",
        "    tf.keras.layers.Dense(57),\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "insurance_model2.compile(loss=tf.keras.losses.mae,\n",
        "                         optimizer= tf.keras.optimizers.Adam(),\n",
        "                         metrics =['mae'])\n",
        "\n",
        "\n",
        "\n",
        "ins = insurance_model2.fit(tf.expand_dims(X_train, -1), y_train, callbacks = [callback], epochs =200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dq1eTUBWrzNo",
        "outputId": "2b237b40-5df4-4cf3-87e1-90cdacf3f9ee"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "34/34 [==============================] - 1s 7ms/step - loss: 12913.5723 - mae: 12913.5723\n",
            "Epoch 2/200\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 12076.0732 - mae: 12076.0732\n",
            "Epoch 3/200\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 11970.5195 - mae: 11970.5195\n",
            "Epoch 4/200\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 11822.4258 - mae: 11822.4258\n",
            "Epoch 5/200\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 11128.4092 - mae: 11128.4092\n",
            "Epoch 6/200\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 8762.1406 - mae: 8762.1406\n",
            "Epoch 7/200\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 8464.1729 - mae: 8464.1729\n",
            "Epoch 8/200\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 8433.5586 - mae: 8433.5586\n",
            "Epoch 9/200\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 8403.7451 - mae: 8403.7451\n",
            "Epoch 10/200\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 8417.0693 - mae: 8417.0693\n",
            "Epoch 11/200\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 8419.2324 - mae: 8419.2324\n",
            "Epoch 12/200\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 8414.5762 - mae: 8414.5762\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insurance_model2.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oI6-WQOAayI",
        "outputId": "38e7278b-0a05-4bdb-a60e-796c12388175"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 4ms/step - loss: 8196.1914 - mae: 8196.1914\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[8196.19140625, 8196.19140625]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "pd.DataFrame(ins.history).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "tU9251NW0WcD",
        "outputId": "25ba659f-3e66-4b78-e990-d4eecaed27af"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc3fdd5d550>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insurance\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "c8yo2cOlK-Lt",
        "outputId": "4c537518-5e36-4f04-f942-2886d6b79927"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age     sex     bmi  children smoker     region      charges\n",
              "0      19  female  27.900         0    yes  southwest  16884.92400\n",
              "1      18    male  33.770         1     no  southeast   1725.55230\n",
              "2      28    male  33.000         3     no  southeast   4449.46200\n",
              "3      33    male  22.705         0     no  northwest  21984.47061\n",
              "4      32    male  28.880         0     no  northwest   3866.85520\n",
              "...   ...     ...     ...       ...    ...        ...          ...\n",
              "1333   50    male  30.970         3     no  northwest  10600.54830\n",
              "1334   18  female  31.920         0     no  northeast   2205.98080\n",
              "1335   18  female  36.850         0     no  southeast   1629.83350\n",
              "1336   21  female  25.800         0     no  southwest   2007.94500\n",
              "1337   61  female  29.070         0    yes  northwest  29141.36030\n",
              "\n",
              "[1338 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-74e49dc5-00f0-4334-8c79-167895267505\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1333</th>\n",
              "      <td>50</td>\n",
              "      <td>male</td>\n",
              "      <td>30.970</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>10600.54830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northeast</td>\n",
              "      <td>2205.98080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1629.83350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>21</td>\n",
              "      <td>female</td>\n",
              "      <td>25.800</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>southwest</td>\n",
              "      <td>2007.94500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>29.070</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>northwest</td>\n",
              "      <td>29141.36030</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-74e49dc5-00f0-4334-8c79-167895267505')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-74e49dc5-00f0-4334-8c79-167895267505 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-74e49dc5-00f0-4334-8c79-167895267505');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Scaling and Normalization"
      ],
      "metadata": {
        "id": "eCD5BM-Nuf3-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn"
      ],
      "metadata": {
        "id": "viqqQ00xzW7c"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# create a column transformer\n",
        "ct = make_column_transformer(\n",
        "    (MinMaxScaler(), [\"age\", \"bmi\", \"children\"]),\n",
        "    (OneHotEncoder(handle_unknown = \"ignore\"), [\"sex\", \"smoker\", \"region\"])\n",
        ")\n",
        "\n",
        "# create features and labels\n",
        "X = insurance.drop('charges', axis =1)\n",
        "y = insurance[\"charges\"]\n",
        "\n",
        "# create test and train set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,\n",
        "                                                    y,\n",
        "                                                    test_size=0.2,\n",
        "                                                    random_state=84)\n",
        "\n",
        "# fit the column transformer to training data\n",
        "ct.fit(X_train)\n",
        "\n",
        "# tranform the train and test with normalization\n",
        "X_train_normal = ct.transform(X_train)\n",
        "X_test_normal = ct.transform(X_test)"
      ],
      "metadata": {
        "id": "ANvijZ9izmLK"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_normal[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_4akrC749sBl",
        "outputId": "db95431b-b6ad-41bb-a68f-7fd1d1a02cdd"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.5      , 0.5759148, 0.4      , 0.       , 1.       , 1.       ,\n",
              "       0.       , 0.       , 1.       , 0.       , 0.       ])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build a neural network model to fit the normalized data"
      ],
      "metadata": {
        "id": "J4U9-GCe97Tt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(84)\n",
        "\n",
        "callback= tf.keras.callbacks.EarlyStopping(monitor = \"mae\", patience = 3)\n",
        "\n",
        "insurance_model3 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "insurance_model3.compile(loss = tf.keras.losses.mae,\n",
        "                         optimizer = tf.keras.optimizers.Adam(),\n",
        "                         metrics = [\"mae\"])\n",
        "\n",
        "insurance_model3.fit(tf.expand_dims(X_train_normal, -1), y_train, callbacks = [callback], epochs = 500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q_Y6r7sP-vHi",
        "outputId": "68f59d09-5866-45ad-aa94-577562b4af6c"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "34/34 [==============================] - 1s 2ms/step - loss: 13361.6543 - mae: 13361.6543\n",
            "Epoch 2/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13359.2666 - mae: 13359.2666\n",
            "Epoch 3/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13353.8193 - mae: 13353.8193\n",
            "Epoch 4/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 13342.5420 - mae: 13342.5420\n",
            "Epoch 5/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13322.0703 - mae: 13322.0703\n",
            "Epoch 6/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13288.9258 - mae: 13288.9258\n",
            "Epoch 7/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13240.1816 - mae: 13240.1816\n",
            "Epoch 8/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13173.1016 - mae: 13173.1016\n",
            "Epoch 9/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13085.0146 - mae: 13085.0146\n",
            "Epoch 10/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 12973.3838 - mae: 12973.3838\n",
            "Epoch 11/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12835.6543 - mae: 12835.6543\n",
            "Epoch 12/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12669.4492 - mae: 12669.4492\n",
            "Epoch 13/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 12474.1035 - mae: 12474.1035\n",
            "Epoch 14/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12249.6641 - mae: 12249.6641\n",
            "Epoch 15/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 12003.6035 - mae: 12003.6035\n",
            "Epoch 16/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 11740.1133 - mae: 11740.1133\n",
            "Epoch 17/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 11465.7119 - mae: 11465.7119\n",
            "Epoch 18/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 11178.1152 - mae: 11178.1152\n",
            "Epoch 19/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 10894.0498 - mae: 10894.0498\n",
            "Epoch 20/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 10616.3213 - mae: 10616.3213\n",
            "Epoch 21/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 10354.6650 - mae: 10354.6650\n",
            "Epoch 22/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 10109.5547 - mae: 10109.5547\n",
            "Epoch 23/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9876.1885 - mae: 9876.1885\n",
            "Epoch 24/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 9668.1787 - mae: 9668.1787\n",
            "Epoch 25/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 9480.6641 - mae: 9480.6641\n",
            "Epoch 26/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9315.8086 - mae: 9315.8086\n",
            "Epoch 27/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9173.9375 - mae: 9173.9375\n",
            "Epoch 28/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9057.4678 - mae: 9057.4678\n",
            "Epoch 29/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8962.1934 - mae: 8962.1934\n",
            "Epoch 30/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8886.2559 - mae: 8886.2559\n",
            "Epoch 31/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8830.5625 - mae: 8830.5625\n",
            "Epoch 32/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8783.5273 - mae: 8783.5273\n",
            "Epoch 33/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8749.8555 - mae: 8749.8555\n",
            "Epoch 34/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8723.0615 - mae: 8723.0615\n",
            "Epoch 35/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8698.7480 - mae: 8698.7480\n",
            "Epoch 36/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8680.3086 - mae: 8680.3086\n",
            "Epoch 37/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8665.1377 - mae: 8665.1377\n",
            "Epoch 38/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8652.4189 - mae: 8652.4189\n",
            "Epoch 39/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8641.4619 - mae: 8641.4619\n",
            "Epoch 40/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8631.1494 - mae: 8631.1494\n",
            "Epoch 41/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8622.1475 - mae: 8622.1475\n",
            "Epoch 42/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8613.1230 - mae: 8613.1230\n",
            "Epoch 43/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8605.5869 - mae: 8605.5869\n",
            "Epoch 44/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8597.1875 - mae: 8597.1875\n",
            "Epoch 45/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8590.1221 - mae: 8590.1221\n",
            "Epoch 46/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8582.8047 - mae: 8582.8047\n",
            "Epoch 47/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8575.6484 - mae: 8575.6484\n",
            "Epoch 48/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8569.1895 - mae: 8569.1895\n",
            "Epoch 49/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8562.2168 - mae: 8562.2168\n",
            "Epoch 50/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8555.9199 - mae: 8555.9199\n",
            "Epoch 51/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8549.0078 - mae: 8549.0078\n",
            "Epoch 52/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8542.7646 - mae: 8542.7646\n",
            "Epoch 53/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8536.9238 - mae: 8536.9238\n",
            "Epoch 54/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8531.8125 - mae: 8531.8125\n",
            "Epoch 55/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8525.3975 - mae: 8525.3975\n",
            "Epoch 56/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8520.5811 - mae: 8520.5811\n",
            "Epoch 57/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8515.9893 - mae: 8515.9893\n",
            "Epoch 58/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8511.7539 - mae: 8511.7539\n",
            "Epoch 59/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8507.0186 - mae: 8507.0186\n",
            "Epoch 60/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8503.1318 - mae: 8503.1318\n",
            "Epoch 61/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8498.9443 - mae: 8498.9443\n",
            "Epoch 62/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8494.3525 - mae: 8494.3525\n",
            "Epoch 63/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8490.9199 - mae: 8490.9199\n",
            "Epoch 64/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8486.6562 - mae: 8486.6562\n",
            "Epoch 65/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8483.5322 - mae: 8483.5322\n",
            "Epoch 66/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8480.3916 - mae: 8480.3916\n",
            "Epoch 67/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8477.1543 - mae: 8477.1543\n",
            "Epoch 68/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8474.0088 - mae: 8474.0088\n",
            "Epoch 69/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8471.0840 - mae: 8471.0840\n",
            "Epoch 70/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8468.1279 - mae: 8468.1279\n",
            "Epoch 71/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8465.2373 - mae: 8465.2373\n",
            "Epoch 72/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8463.1445 - mae: 8463.1445\n",
            "Epoch 73/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8460.4287 - mae: 8460.4287\n",
            "Epoch 74/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8456.9941 - mae: 8456.9941\n",
            "Epoch 75/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8454.2939 - mae: 8454.2939\n",
            "Epoch 76/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8451.8154 - mae: 8451.8154\n",
            "Epoch 77/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8449.2578 - mae: 8449.2578\n",
            "Epoch 78/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8447.3564 - mae: 8447.3564\n",
            "Epoch 79/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8446.7412 - mae: 8446.7412\n",
            "Epoch 80/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8443.0771 - mae: 8443.0771\n",
            "Epoch 81/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8441.9404 - mae: 8441.9404\n",
            "Epoch 82/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8439.4297 - mae: 8439.4297\n",
            "Epoch 83/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8437.3760 - mae: 8437.3760\n",
            "Epoch 84/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8435.6396 - mae: 8435.6396\n",
            "Epoch 85/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8433.9395 - mae: 8433.9395\n",
            "Epoch 86/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8432.4775 - mae: 8432.4775\n",
            "Epoch 87/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8432.5088 - mae: 8432.5088\n",
            "Epoch 88/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8429.9678 - mae: 8429.9678\n",
            "Epoch 89/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8428.4238 - mae: 8428.4238\n",
            "Epoch 90/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8427.5479 - mae: 8427.5479\n",
            "Epoch 91/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8426.1738 - mae: 8426.1738\n",
            "Epoch 92/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8424.7812 - mae: 8424.7812\n",
            "Epoch 93/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8423.6533 - mae: 8423.6533\n",
            "Epoch 94/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8422.7139 - mae: 8422.7139\n",
            "Epoch 95/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8422.1592 - mae: 8422.1592\n",
            "Epoch 96/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8420.7422 - mae: 8420.7422\n",
            "Epoch 97/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8420.1055 - mae: 8420.1055\n",
            "Epoch 98/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8419.2109 - mae: 8419.2109\n",
            "Epoch 99/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8418.9492 - mae: 8418.9492\n",
            "Epoch 100/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8418.1123 - mae: 8418.1123\n",
            "Epoch 101/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8417.7148 - mae: 8417.7148\n",
            "Epoch 102/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8417.2236 - mae: 8417.2236\n",
            "Epoch 103/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8416.4570 - mae: 8416.4570\n",
            "Epoch 104/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8416.1191 - mae: 8416.1191\n",
            "Epoch 105/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8415.5615 - mae: 8415.5615\n",
            "Epoch 106/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8415.1230 - mae: 8415.1230\n",
            "Epoch 107/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8416.4961 - mae: 8416.4961\n",
            "Epoch 108/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8414.0342 - mae: 8414.0342\n",
            "Epoch 109/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8414.1016 - mae: 8414.1016\n",
            "Epoch 110/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8413.4707 - mae: 8413.4707\n",
            "Epoch 111/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8413.8770 - mae: 8413.8770\n",
            "Epoch 112/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8413.7451 - mae: 8413.7451\n",
            "Epoch 113/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8413.0576 - mae: 8413.0576\n",
            "Epoch 114/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8412.5957 - mae: 8412.5957\n",
            "Epoch 115/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8412.3574 - mae: 8412.3574\n",
            "Epoch 116/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8412.5312 - mae: 8412.5312\n",
            "Epoch 117/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8412.0479 - mae: 8412.0479\n",
            "Epoch 118/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8411.5469 - mae: 8411.5469\n",
            "Epoch 119/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8411.5908 - mae: 8411.5908\n",
            "Epoch 120/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8411.1270 - mae: 8411.1270\n",
            "Epoch 121/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8411.7373 - mae: 8411.7373\n",
            "Epoch 122/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8410.8789 - mae: 8410.8789\n",
            "Epoch 123/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8410.4492 - mae: 8410.4492\n",
            "Epoch 124/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8410.7070 - mae: 8410.7070\n",
            "Epoch 125/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8410.2803 - mae: 8410.2803\n",
            "Epoch 126/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8410.0293 - mae: 8410.0293\n",
            "Epoch 127/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8410.6670 - mae: 8410.6670\n",
            "Epoch 128/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8409.9980 - mae: 8409.9980\n",
            "Epoch 129/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8409.9248 - mae: 8409.9248\n",
            "Epoch 130/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8410.4375 - mae: 8410.4375\n",
            "Epoch 131/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8409.4941 - mae: 8409.4941\n",
            "Epoch 132/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8409.3613 - mae: 8409.3613\n",
            "Epoch 133/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8409.0557 - mae: 8409.0557\n",
            "Epoch 134/500\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8409.5293 - mae: 8409.5293\n",
            "Epoch 135/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8409.2705 - mae: 8409.2705\n",
            "Epoch 136/500\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8409.2744 - mae: 8409.2744\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc404c3c3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate on normalized data\n",
        "insurance_model3.evaluate(X_test_normal, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkNHZROW_rjo",
        "outputId": "e1276f3d-ae2b-4ced-e6bb-a686427aef9f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 8093.7266 - mae: 8093.7266\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[8093.7265625, 8093.7265625]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "save model\n"
      ],
      "metadata": {
        "id": "ZEFp0_yEAItp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "insurance_model3.save(\"Medical_Insurance_model\")"
      ],
      "metadata": {
        "id": "SDMigutqGZt6"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load model"
      ],
      "metadata": {
        "id": "Z5uUR0AAGq8E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "insurance_loaded = tf.keras.models.load_model(\"/content/Medical Insurance model\")"
      ],
      "metadata": {
        "id": "vxOYV4QxG1HC"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "insurance_loaded.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eDwIyxt3HAPU",
        "outputId": "9bbc43e8-7777-42d2-cd63-40aace6dce69"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_24 (Dense)            (None, 11, 100)           200       \n",
            "                                                                 \n",
            " dense_25 (Dense)            (None, 11, 10)            1010      \n",
            "                                                                 \n",
            " dense_26 (Dense)            (None, 11, 1)             11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,221\n",
            "Trainable params: 1,221\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Save to colab"
      ],
      "metadata": {
        "id": "yGsvggsnHDnj"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xJWh7Uo4JWLR"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}