regression_problem_01.ipynb

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        "<a href=\"https://colab.research.google.com/github/ShauryaJain03/Deep-Learning-Tensorflow/blob/main/regression_problem_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "yr-KfpgHWNxu"
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      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")"
      ],
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        "id": "qRawDJm6WjUb"
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      "execution_count": 30,
      "outputs": []
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      "source": [
        "insurance.head()"
      ],
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              "   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"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#one hot encode the categorical data\n",
        "insurance_data=pd.get_dummies(insurance)\n",
        "insurance_data"
      ],
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        "colab": {
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          "height": 423
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        "id": "ixTsHB1fYJ1I",
        "outputId": "2c5c5405-cf34-49e8-85d1-fb9b486f680b"
      },
      "execution_count": 32,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      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",
              "2      28  33.000         3   4449.46200           0         1          1   \n",
              "3      33  22.705         0  21984.47061           0         1          1   \n",
              "4      32  28.880         0   3866.85520           0         1          1   \n",
              "...   ...     ...       ...          ...         ...       ...        ...   \n",
              "1333   50  30.970         3  10600.54830           0         1          1   \n",
              "1334   18  31.920         0   2205.98080           1         0          1   \n",
              "1335   18  36.850         0   1629.83350           1         0          1   \n",
              "1336   21  25.800         0   2007.94500           1         0          1   \n",
              "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",
              "1              0                 0                 0                 1   \n",
              "2              0                 0                 0                 1   \n",
              "3              0                 0                 1                 0   \n",
              "4              0                 0                 1                 0   \n",
              "...          ...               ...               ...               ...   \n",
              "1333           0                 0                 1                 0   \n",
              "1334           0                 1                 0                 0   \n",
              "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",
              "1                    0  \n",
              "2                    0  \n",
              "3                    0  \n",
              "4                    0  \n",
              "...                ...  \n",
              "1333                 0  \n",
              "1334                 0  \n",
              "1335                 0  \n",
              "1336                 1  \n",
              "1337                 0  \n",
              "\n",
              "[1338 rows x 12 columns]"
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1333</th>\n",
              "      <td>50</td>\n",
              "      <td>30.970</td>\n",
              "      <td>3</td>\n",
              "      <td>10600.54830</td>\n",
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              "      <td>1</td>\n",
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              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>2205.98080</td>\n",
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              "      <th>1336</th>\n",
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              "    <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",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=insurance_data.drop(\"charges\",axis=1)\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "jWJeENojYpFh",
        "outputId": "c64c98d2-0b0d-455b-9569-57d6fd010312"
      },
      "execution_count": 33,
      "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",
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              "1337   61  29.070         0           1         0          0           1   \n",
              "\n",
              "      region_northeast  region_northwest  region_southeast  region_southwest  \n",
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              "1335                 0                 0                 1                 0  \n",
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              "1337                 0                 1                 0                 0  \n",
              "\n",
              "[1338 rows x 11 columns]"
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              "      <td>50</td>\n",
              "      <td>30.970</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>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>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>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 × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00319ce9-edf6-44cb-98c7-cde7770fdc52')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-00319ce9-edf6-44cb-98c7-cde7770fdc52 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-00319ce9-edf6-44cb-98c7-cde7770fdc52');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e76024eb-ff4e-4161-93ae-7676f4e3decf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e76024eb-ff4e-4161-93ae-7676f4e3decf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e76024eb-ff4e-4161-93ae-7676f4e3decf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_cc1675a1-c2db-414d-9e7e-da1834923421\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_cc1675a1-c2db-414d-9e7e-da1834923421 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=insurance_data[\"charges\"]\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iP7kES0DZLE1",
        "outputId": "565548f2-36df-4432-9285-6854c2ad0399"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       16884.92400\n",
              "1        1725.55230\n",
              "2        4449.46200\n",
              "3       21984.47061\n",
              "4        3866.85520\n",
              "           ...     \n",
              "1333    10600.54830\n",
              "1334     2205.98080\n",
              "1335     1629.83350\n",
              "1336     2007.94500\n",
              "1337    29141.36030\n",
              "Name: charges, Length: 1338, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.20,random_state=42)"
      ],
      "metadata": {
        "id": "51XKHWKgZ8sW"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_1=tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(1),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model_1.compile(loss=\"mae\",\n",
        "                optimizer=\"SGD\",\n",
        "                metrics=[\"mae\"])\n",
        "\n",
        "\n",
        "model_1.fit(x_train,y_train,epochs=100,verbose=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSbyHKuTadLy",
        "outputId": "f0e304c6-269b-4244-fece-1dccf0e06f25"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e7f517eac50>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.evaluate(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKSI_kLLbIxI",
        "outputId": "e2e3352b-75c6-4f3c-e154-c5bc5906c950"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 9942.3398 - mae: 9942.3398\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9942.33984375, 9942.33984375]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2=tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model_2.compile(loss=\"mae\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"mae\"])\n",
        "\n",
        "\n",
        "h_2=model_2.fit(x_train,y_train,epochs=200,verbose=0)"
      ],
      "metadata": {
        "id": "x-0noIaAbhat"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.evaluate(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Im5Be44Ebwc4",
        "outputId": "593b19b0-739d-4333-ba0a-5a6ddfafe4a6"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 3514.3340 - mae: 3514.3340\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3514.333984375, 3514.333984375]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(h_2.history).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "QmfL0a5tcam0",
        "outputId": "b490c8f2-60d0-4762-ef1a-d28730466205"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h_3=model_2.fit(x_train,y_train,epochs=200,verbose=0)"
      ],
      "metadata": {
        "id": "t9tSfp1gc3FD"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.evaluate(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R73BqmOzd4Wb",
        "outputId": "b2d66579-e144-45e7-a757-1eac4a3abdaa"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step - loss: 3161.5349 - mae: 3161.5349\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3161.534912109375, 3161.534912109375]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A common practice when working with neural networks is to make sure all of the data you pass to them is in the range 0 to 1.\n",
        "\n",
        "This practice is called normalization (scaling all values from their original range to, e.g. between 0 and 100,000 to be between 0 and 1).\n",
        "\n",
        "There is another process call standardization which converts all of your data to unit variance and 0 mean.\n",
        "\n",
        "These two practices are often part of a preprocessing pipeline (a series of functions to prepare your data for use with neural networks).\n",
        "\n",
        "Knowing this, some of the major steps you'll take to preprocess your data for a neural network include:\n",
        "\n",
        "Turning all of your data to numbers (a neural network can't handle strings).\n",
        "\n",
        "Making sure your data is in the right shape (verifying input and output shapes).\n",
        "\n",
        "Feature scaling:\n",
        "\n",
        "Normalizing data (making sure all values are between 0 and 1). This is done by subtracting the minimum value then dividing by the maximum value minus the minimum. This is also referred to as min-max scaling.\n",
        "\n",
        "Standardization (making sure all values have a mean of 0 and a variance of 1). This is done by subtracting the mean value from the target feature and then dividing it by the standard deviation.\n",
        "Which one should you use?\n",
        "With neural networks you'll tend to favour normalization as they tend to prefer values between 0 and 1 (you'll see this espcially with image processing), however, you'll often find a neural network can perform pretty well with minimal feature scaling."
      ],
      "metadata": {
        "id": "kYJEX-JrmNQh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#preprocessing the data\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "# Read in the insurance dataset\n",
        "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")"
      ],
      "metadata": {
        "id": "73QFoQCpdg6E"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "\n",
        "# Create column transformer (this will help us normalize/preprocess our data)\n",
        "ct = make_column_transformer(\n",
        "    (MinMaxScaler(), [\"age\", \"bmi\", \"children\"]), # get all values between 0 and 1\n",
        "    (OneHotEncoder(handle_unknown=\"ignore\"), [\"sex\", \"smoker\", \"region\"])\n",
        ")\n",
        "\n",
        "# Create X & y\n",
        "x = insurance.drop(\"charges\", axis=1)\n",
        "y = insurance[\"charges\"]\n",
        "\n",
        "# Build our train and test sets (use random state to ensure same split as before)\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Fit column transformer on the training data only (doing so on test data would result in data leakage)\n",
        "ct.fit(x_train)\n",
        "\n",
        "# Transform training and test data with normalization (MinMaxScalar) and one hot encoding (OneHotEncoder)\n",
        "x_train_normal = ct.transform(x_train)\n",
        "x_test_normal = ct.transform(x_test)\n",
        ""
      ],
      "metadata": {
        "id": "rxs50-YqhiKs"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "es_nDtqHi5wB",
        "outputId": "0aff343b-bf58-4cbd-f284-cd36d53c06c3"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age     sex     bmi  children smoker     region\n",
              "0      19  female  27.900         0    yes  southwest\n",
              "1      18    male  33.770         1     no  southeast\n",
              "2      28    male  33.000         3     no  southeast\n",
              "3      33    male  22.705         0     no  northwest\n",
              "4      32    male  28.880         0     no  northwest\n",
              "...   ...     ...     ...       ...    ...        ...\n",
              "1333   50    male  30.970         3     no  northwest\n",
              "1334   18  female  31.920         0     no  northeast\n",
              "1335   18  female  36.850         0     no  southeast\n",
              "1336   21  female  25.800         0     no  southwest\n",
              "1337   61  female  29.070         0    yes  northwest\n",
              "\n",
              "[1338 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-68dfefc1-6637-4b37-8d65-1078954ffd4a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-68dfefc1-6637-4b37-8d65-1078954ffd4a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-68dfefc1-6637-4b37-8d65-1078954ffd4a 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-68dfefc1-6637-4b37-8d65-1078954ffd4a');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9bd278fe-d209-4833-8202-a3491956a55f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9bd278fe-d209-4833-8202-a3491956a55f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8d526879-7096-4d0f-986c-edaff7abb2fc\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8d526879-7096-4d0f-986c-edaff7abb2fc button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, just as before, we need to transform the non-numerical columns into numbers and this time we'll also be normalizing the numerical columns with different ranges (to make sure they're all between 0 and 1).\n",
        "\n",
        "To do this, we're going to use a few classes from Scikit-Learn:\n",
        "\n",
        "make_column_transformer - build a multi-step data preprocessing function for the folllowing transformations:\n",
        "MinMaxScaler - make sure all numerical columns are normalized (between 0 and 1).\n",
        "OneHotEncoder - one hot encode the non-numerical columns.\n",
        "Let's see them in action."
      ],
      "metadata": {
        "id": "ZnNBLshsmHML"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_normal.shape , x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4sxl0NFisC7",
        "outputId": "3b0138ab-0305-4638-af39-dd00f659f82f"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1070, 11), (1070, 6))"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3=tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model_3.compile(loss=\"mae\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=['mae'])\n",
        "\n",
        "h_4=model_3.fit(x_train_normal,y_train,epochs=200,verbose=0)"
      ],
      "metadata": {
        "id": "836H6RW8jKV4"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.evaluate(x_test_normal,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_6yMO_Ek_rb",
        "outputId": "673c2700-3974-45e1-b70a-1b793c9cb88e"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 3164.3374 - mae: 3164.3374\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3164.33740234375, 3164.33740234375]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(h_4.history).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "h0pi9VnXl5_h",
        "outputId": "9eccd2b1-959e-4b89-95e0-f63180388163"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}