Boston_Housing_Price_Prediction_with_a_Deep_Neural_Network.ipynb

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      "name": "Boston_Housing_Price_Prediction_with_a_Deep_Neural_Network.ipynb",
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    "accelerator": "GPU",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Import Library"
      ],
      "metadata": {
        "id": "YOTzZhdR9zZt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data analysis and visualization\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline \n",
        "\n",
        "# Preprocessing and evaluation\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "_F2vhnLJ3lm6"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Data"
      ],
      "metadata": {
        "id": "YhqBbskr-iag"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train , y_train) , (X_test , y_test) = tf.keras.datasets.boston_housing.load_data(path = 'boston_housing_npz' , \n",
        "                                                                           test_split = 0.2 , seed = 42)"
      ],
      "metadata": {
        "id": "ILfTB4Ow3OAU",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96cf0938-0070-41b5-d59d-d32f2fda7e6e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/boston_housing.npz\n",
            "57344/57026 [==============================] - 0s 0us/step\n",
            "65536/57026 [==================================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "hHPrTJsyHKSO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initial Observation"
      ],
      "metadata": {
        "id": "iFxpm9uu-mcs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the data shape and type\n",
        "(X_train.shape, type(X_train)), (X_test.shape, type(X_test)), (y_train.shape, type(y_train)), (y_test.shape, type(y_test)),"
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      "cell_type": "code",
      "source": [
        "# Converting Data to DataFrame \n",
        "X_train_df = pd.DataFrame(X_train)\n",
        "y_train_df = pd.DataFrame(y_train)\n",
        "\n",
        "# Preview the training data\n",
        "X_train_df.head(10)"
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              "        0     1      2    3      4      5     6       7     8      9     10  \\\n",
              "0  0.09178   0.0   4.05  0.0  0.510  6.416  84.1  2.6463   5.0  296.0  16.6   \n",
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              "3  0.09164   0.0  10.81  0.0  0.413  6.065   7.8  5.2873   4.0  305.0  19.2   \n",
              "4  5.09017   0.0  18.10  0.0  0.713  6.297  91.8  2.3682  24.0  666.0  20.2   \n",
              "5  0.10153   0.0  12.83  0.0  0.437  6.279  74.5  4.0522   5.0  398.0  18.7   \n",
              "6  0.31827   0.0   9.90  0.0  0.544  5.914  83.2  3.9986   4.0  304.0  18.4   \n",
              "7  0.29090   0.0  21.89  0.0  0.624  6.174  93.6  1.6119   4.0  437.0  21.2   \n",
              "8  4.03841   0.0  18.10  0.0  0.532  6.229  90.7  3.0993  24.0  666.0  20.2   \n",
              "9  0.22438   0.0   9.69  0.0  0.585  6.027  79.7  2.4982   6.0  391.0  19.2   \n",
              "\n",
              "       11     12  \n",
              "0  395.50   9.04  \n",
              "1  396.90   3.53  \n",
              "2  390.11  18.07  \n",
              "3  390.91   5.52  \n",
              "4  385.09  17.27  \n",
              "5  373.66  11.97  \n",
              "6  390.70  18.33  \n",
              "7  388.08  24.16  \n",
              "8  395.33  12.87  \n",
              "9  396.90  14.33  "
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    {
      "cell_type": "code",
      "source": [
        "# View summary of datasets\n",
        "X_train_df.info()\n",
        "print('_'*40)\n",
        "y_train_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NtocpgHS6zUg",
        "outputId": "874e6070-2117-49b5-968e-44f980f9219e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 404 entries, 0 to 403\n",
            "Data columns (total 13 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       404 non-null    float64\n",
            " 1   1       404 non-null    float64\n",
            " 2   2       404 non-null    float64\n",
            " 3   3       404 non-null    float64\n",
            " 4   4       404 non-null    float64\n",
            " 5   5       404 non-null    float64\n",
            " 6   6       404 non-null    float64\n",
            " 7   7       404 non-null    float64\n",
            " 8   8       404 non-null    float64\n",
            " 9   9       404 non-null    float64\n",
            " 10  10      404 non-null    float64\n",
            " 11  11      404 non-null    float64\n",
            " 12  12      404 non-null    float64\n",
            "dtypes: float64(13)\n",
            "memory usage: 41.2 KB\n",
            "________________________________________\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 404 entries, 0 to 403\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       404 non-null    float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 3.3 KB\n"
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    {
      "cell_type": "code",
      "source": [
        "# distribution of numerical feature values across the samples\n",
        "X_train_df.describe()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "CeeFStx68vMP",
        "outputId": "887bb519-8460-4c38-f52a-e7c65e66a221"
      },
      "execution_count": 7,
      "outputs": [
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              "               0           1           2           3           4           5   \\\n",
              "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
              "mean     3.789989   11.568069   11.214059    0.069307    0.554524    6.284824   \n",
              "std      9.132761   24.269648    6.925462    0.254290    0.116408    0.723759   \n",
              "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
              "25%      0.081960    0.000000    5.190000    0.000000    0.452000    5.878750   \n",
              "50%      0.262660    0.000000    9.690000    0.000000    0.538000    6.210000   \n",
              "75%      3.717875   12.500000   18.100000    0.000000    0.624000    6.620500   \n",
              "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
              "\n",
              "               6           7           8           9           10          11  \\\n",
              "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
              "mean    69.119307    3.792258    9.660891  408.960396   18.481931  356.293020   \n",
              "std     28.034606    2.142651    8.736073  169.685166    2.157322   92.058615   \n",
              "min      2.900000    1.137000    1.000000  187.000000   12.600000    0.320000   \n",
              "25%     45.475000    2.097050    4.000000  281.000000   17.400000  375.085000   \n",
              "50%     77.500000    3.167500    5.000000  330.000000   19.100000  391.305000   \n",
              "75%     94.425000    5.118000   24.000000  666.000000   20.200000  395.810000   \n",
              "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
              "\n",
              "               12  \n",
              "count  404.000000  \n",
              "mean    12.825520  \n",
              "std      7.308772  \n",
              "min      1.920000  \n",
              "25%      7.092500  \n",
              "50%     11.560000  \n",
              "75%     17.167500  \n",
              "max     37.970000  "
            ],
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              "\n",
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              "      <th>12</th>\n",
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              "      <th>count</th>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
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              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>3.789989</td>\n",
              "      <td>11.568069</td>\n",
              "      <td>11.214059</td>\n",
              "      <td>0.069307</td>\n",
              "      <td>0.554524</td>\n",
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              "      <td>3.792258</td>\n",
              "      <td>9.660891</td>\n",
              "      <td>408.960396</td>\n",
              "      <td>18.481931</td>\n",
              "      <td>356.293020</td>\n",
              "      <td>12.825520</td>\n",
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              "      <th>std</th>\n",
              "      <td>9.132761</td>\n",
              "      <td>24.269648</td>\n",
              "      <td>6.925462</td>\n",
              "      <td>0.254290</td>\n",
              "      <td>0.116408</td>\n",
              "      <td>0.723759</td>\n",
              "      <td>28.034606</td>\n",
              "      <td>2.142651</td>\n",
              "      <td>8.736073</td>\n",
              "      <td>169.685166</td>\n",
              "      <td>2.157322</td>\n",
              "      <td>92.058615</td>\n",
              "      <td>7.308772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.137000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.081960</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.452000</td>\n",
              "      <td>5.878750</td>\n",
              "      <td>45.475000</td>\n",
              "      <td>2.097050</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>281.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.085000</td>\n",
              "      <td>7.092500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.262660</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.210000</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.167500</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.100000</td>\n",
              "      <td>391.305000</td>\n",
              "      <td>11.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.717875</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.620500</td>\n",
              "      <td>94.425000</td>\n",
              "      <td>5.118000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>395.810000</td>\n",
              "      <td>17.167500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "      <td>37.970000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing"
      ],
      "metadata": {
        "id": "0S3DE2_c_8j2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create column transformer\n",
        "ct = make_column_transformer(\n",
        "    (MinMaxScaler(), [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
        ")\n",
        "\n",
        "# Normalization and data type change\n",
        "X_train = ct.fit_transform(X_train).astype('float32')\n",
        "X_test = ct.transform(X_test).astype('float32')\n",
        "y_train = y_train.astype('float32')\n",
        "y_test = y_test.astype('float32')\n",
        "\n",
        "# Distribution of X_train feature values after normalization\n",
        "pd.DataFrame(X_train).describe()"
      ],
      "metadata": {
        "id": "JEXlXchREDC6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "outputId": "891b630d-c9f4-4c11-9f09-dbbf883324d6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               0           1           2           3           4           5   \\\n",
              "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
              "mean     0.042528    0.115681    0.394210    0.348815    0.521905    0.681970   \n",
              "std      0.102650    0.242696    0.253866    0.239522    0.138678    0.288719   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000850    0.000000    0.173387    0.137860    0.444098    0.438466   \n",
              "50%      0.002881    0.000000    0.338343    0.314815    0.507569    0.768280   \n",
              "75%      0.041717    0.125000    0.646628    0.491770    0.586223    0.942585   \n",
              "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
              "\n",
              "               6           7           8           9           10          11  \n",
              "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000  \n",
              "mean     0.241618    0.376560    0.423589    0.625737    0.897607    0.302511  \n",
              "std      0.194973    0.379829    0.323827    0.229502    0.232131    0.202740  \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
              "25%      0.087361    0.130435    0.179389    0.510638    0.944992    0.143481  \n",
              "50%      0.184767    0.173913    0.272901    0.691489    0.985892    0.267406  \n",
              "75%      0.362255    1.000000    0.914122    0.808511    0.997252    0.422954  \n",
              "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000  "
            ],
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              "\n",
              "  <div id=\"df-d3d88d93-0050-4ed5-b06e-4f632dbb0761\">\n",
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              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
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              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>404.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.042528</td>\n",
              "      <td>0.115681</td>\n",
              "      <td>0.394210</td>\n",
              "      <td>0.348815</td>\n",
              "      <td>0.521905</td>\n",
              "      <td>0.681970</td>\n",
              "      <td>0.241618</td>\n",
              "      <td>0.376560</td>\n",
              "      <td>0.423589</td>\n",
              "      <td>0.625737</td>\n",
              "      <td>0.897607</td>\n",
              "      <td>0.302511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.102650</td>\n",
              "      <td>0.242696</td>\n",
              "      <td>0.253866</td>\n",
              "      <td>0.239522</td>\n",
              "      <td>0.138678</td>\n",
              "      <td>0.288719</td>\n",
              "      <td>0.194973</td>\n",
              "      <td>0.379829</td>\n",
              "      <td>0.323827</td>\n",
              "      <td>0.229502</td>\n",
              "      <td>0.232131</td>\n",
              "      <td>0.202740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000850</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.173387</td>\n",
              "      <td>0.137860</td>\n",
              "      <td>0.444098</td>\n",
              "      <td>0.438466</td>\n",
              "      <td>0.087361</td>\n",
              "      <td>0.130435</td>\n",
              "      <td>0.179389</td>\n",
              "      <td>0.510638</td>\n",
              "      <td>0.944992</td>\n",
              "      <td>0.143481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.002881</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.338343</td>\n",
              "      <td>0.314815</td>\n",
              "      <td>0.507569</td>\n",
              "      <td>0.768280</td>\n",
              "      <td>0.184767</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.272901</td>\n",
              "      <td>0.691489</td>\n",
              "      <td>0.985892</td>\n",
              "      <td>0.267406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.041717</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.646628</td>\n",
              "      <td>0.491770</td>\n",
              "      <td>0.586223</td>\n",
              "      <td>0.942585</td>\n",
              "      <td>0.362255</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.914122</td>\n",
              "      <td>0.808511</td>\n",
              "      <td>0.997252</td>\n",
              "      <td>0.422954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3d88d93-0050-4ed5-b06e-4f632dbb0761')\"\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-d3d88d93-0050-4ed5-b06e-4f632dbb0761 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-d3d88d93-0050-4ed5-b06e-4f632dbb0761');\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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model, Predict, Evaluation"
      ],
      "metadata": {
        "id": "EwbgGLK9FyRG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Reserve data for validation\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=42)\n",
        "X_train.shape, X_val.shape, y_train.shape, y_val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcf22498-58a2-405e-f110-e0def71cd652",
        "id": "0yG6aN-_RDxF"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((363, 12), (41, 12), (363,), (41,))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating the Model and Optimizing the Learning Rate"
      ],
      "metadata": {
        "id": "rXXjkEnchKQ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Building the model\n",
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(units=10, activation='relu', name='Dense_1'),\n",
        "  tf.keras.layers.Dense(units=100, activation='relu', name='Dense_2'),\n",
        "  tf.keras.layers.Dense(units=1, name='Prediction')\n",
        "])\n",
        "\n",
        "# Compiling the model\n",
        "model.compile(loss = tf.keras.losses.mean_absolute_error,\n",
        "              optimizer = tf.keras.optimizers.Adam(),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Create the learning rate callback\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10**(epoch/20))\n",
        "\n",
        "# Training the model\n",
        "history = model.fit(X_train,\n",
        "                    y_train,\n",
        "                    batch_size=32,\n",
        "                    epochs=100,\n",
        "                    callbacks=[lr_scheduler],\n",
        "                    validation_data=(X_val, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vSfppD_ghQVg",
        "outputId": "dfd59556-c990-4fd4-f6fc-609e9f1e3d32"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 21.9218 - mae: 21.9218 - val_loss: 24.3418 - val_mae: 24.3418 - lr: 0.0010\n",
            "Epoch 2/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 21.4702 - mae: 21.4702 - val_loss: 23.7764 - val_mae: 23.7764 - lr: 0.0011\n",
            "Epoch 3/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 20.7561 - mae: 20.7561 - val_loss: 22.8233 - val_mae: 22.8233 - lr: 0.0013\n",
            "Epoch 4/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 19.5152 - mae: 19.5152 - val_loss: 21.1427 - val_mae: 21.1427 - lr: 0.0014\n",
            "Epoch 5/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 17.2878 - mae: 17.2878 - val_loss: 18.0222 - val_mae: 18.0222 - lr: 0.0016\n",
            "Epoch 6/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 13.4218 - mae: 13.4218 - val_loss: 12.9908 - val_mae: 12.9908 - lr: 0.0018\n",
            "Epoch 7/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 9.3467 - mae: 9.3467 - val_loss: 9.6701 - val_mae: 9.6701 - lr: 0.0020\n",
            "Epoch 8/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 8.3166 - mae: 8.3166 - val_loss: 8.9941 - val_mae: 8.9941 - lr: 0.0022\n",
            "Epoch 9/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 7.5411 - mae: 7.5411 - val_loss: 8.3168 - val_mae: 8.3168 - lr: 0.0025\n",
            "Epoch 10/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.7576 - mae: 6.7576 - val_loss: 7.5559 - val_mae: 7.5559 - lr: 0.0028\n",
            "Epoch 11/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 5.9872 - mae: 5.9872 - val_loss: 6.7440 - val_mae: 6.7440 - lr: 0.0032\n",
            "Epoch 12/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 5.4054 - mae: 5.4054 - val_loss: 6.3285 - val_mae: 6.3285 - lr: 0.0035\n",
            "Epoch 13/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 5.0126 - mae: 5.0126 - val_loss: 5.9403 - val_mae: 5.9403 - lr: 0.0040\n",
            "Epoch 14/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 4.7996 - mae: 4.7996 - val_loss: 5.7663 - val_mae: 5.7663 - lr: 0.0045\n",
            "Epoch 15/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.6271 - mae: 4.6271 - val_loss: 5.5196 - val_mae: 5.5196 - lr: 0.0050\n",
            "Epoch 16/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.4849 - mae: 4.4849 - val_loss: 5.3127 - val_mae: 5.3127 - lr: 0.0056\n",
            "Epoch 17/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.2739 - mae: 4.2739 - val_loss: 5.0344 - val_mae: 5.0344 - lr: 0.0063\n",
            "Epoch 18/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.1037 - mae: 4.1037 - val_loss: 4.7524 - val_mae: 4.7524 - lr: 0.0071\n",
            "Epoch 19/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.8519 - mae: 3.8519 - val_loss: 4.3595 - val_mae: 4.3595 - lr: 0.0079\n",
            "Epoch 20/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.6198 - mae: 3.6198 - val_loss: 3.8633 - val_mae: 3.8633 - lr: 0.0089\n",
            "Epoch 21/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.3029 - mae: 3.3029 - val_loss: 3.4909 - val_mae: 3.4909 - lr: 0.0100\n",
            "Epoch 22/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.2093 - mae: 3.2093 - val_loss: 3.2547 - val_mae: 3.2547 - lr: 0.0112\n",
            "Epoch 23/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 3.6939 - mae: 3.6939 - val_loss: 3.2226 - val_mae: 3.2226 - lr: 0.0126\n",
            "Epoch 24/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 2.9217 - mae: 2.9217 - val_loss: 3.3208 - val_mae: 3.3208 - lr: 0.0141\n",
            "Epoch 25/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.8956 - mae: 2.8956 - val_loss: 3.1274 - val_mae: 3.1274 - lr: 0.0158\n",
            "Epoch 26/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.9272 - mae: 2.9272 - val_loss: 4.5589 - val_mae: 4.5589 - lr: 0.0178\n",
            "Epoch 27/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.1293 - mae: 3.1293 - val_loss: 2.8226 - val_mae: 2.8226 - lr: 0.0200\n",
            "Epoch 28/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.9610 - mae: 2.9610 - val_loss: 2.9049 - val_mae: 2.9049 - lr: 0.0224\n",
            "Epoch 29/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.9909 - mae: 2.9909 - val_loss: 2.8062 - val_mae: 2.8062 - lr: 0.0251\n",
            "Epoch 30/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.6829 - mae: 2.6829 - val_loss: 2.4505 - val_mae: 2.4505 - lr: 0.0282\n",
            "Epoch 31/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 2.6276 - mae: 2.6276 - val_loss: 3.5904 - val_mae: 3.5904 - lr: 0.0316\n",
            "Epoch 32/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.7403 - mae: 2.7403 - val_loss: 2.8934 - val_mae: 2.8934 - lr: 0.0355\n",
            "Epoch 33/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.1330 - mae: 3.1330 - val_loss: 2.4343 - val_mae: 2.4343 - lr: 0.0398\n",
            "Epoch 34/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.0871 - mae: 3.0871 - val_loss: 2.4314 - val_mae: 2.4314 - lr: 0.0447\n",
            "Epoch 35/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.9014 - mae: 2.9014 - val_loss: 2.9245 - val_mae: 2.9245 - lr: 0.0501\n",
            "Epoch 36/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.9166 - mae: 2.9166 - val_loss: 3.6925 - val_mae: 3.6925 - lr: 0.0562\n",
            "Epoch 37/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.7079 - mae: 2.7079 - val_loss: 3.6001 - val_mae: 3.6001 - lr: 0.0631\n",
            "Epoch 38/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.3286 - mae: 3.3286 - val_loss: 3.0715 - val_mae: 3.0715 - lr: 0.0708\n",
            "Epoch 39/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.0176 - mae: 3.0176 - val_loss: 2.6366 - val_mae: 2.6366 - lr: 0.0794\n",
            "Epoch 40/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.8105 - mae: 2.8105 - val_loss: 2.5076 - val_mae: 2.5076 - lr: 0.0891\n",
            "Epoch 41/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 2.9151 - mae: 2.9151 - val_loss: 3.2010 - val_mae: 3.2010 - lr: 0.1000\n",
            "Epoch 42/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.1130 - mae: 4.1130 - val_loss: 3.0031 - val_mae: 3.0031 - lr: 0.1122\n",
            "Epoch 43/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.6808 - mae: 3.6808 - val_loss: 2.5383 - val_mae: 2.5383 - lr: 0.1259\n",
            "Epoch 44/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.3694 - mae: 3.3694 - val_loss: 4.8563 - val_mae: 4.8563 - lr: 0.1413\n",
            "Epoch 45/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 5.3112 - mae: 5.3112 - val_loss: 3.0150 - val_mae: 3.0150 - lr: 0.1585\n",
            "Epoch 46/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 4.2439 - mae: 4.2439 - val_loss: 6.6989 - val_mae: 6.6989 - lr: 0.1778\n",
            "Epoch 47/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.8144 - mae: 3.8144 - val_loss: 2.8961 - val_mae: 2.8961 - lr: 0.1995\n",
            "Epoch 48/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.5791 - mae: 3.5791 - val_loss: 2.7433 - val_mae: 2.7433 - lr: 0.2239\n",
            "Epoch 49/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.4412 - mae: 3.4412 - val_loss: 4.1671 - val_mae: 4.1671 - lr: 0.2512\n",
            "Epoch 50/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.0623 - mae: 4.0623 - val_loss: 3.2043 - val_mae: 3.2043 - lr: 0.2818\n",
            "Epoch 51/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.8895 - mae: 4.8895 - val_loss: 5.4729 - val_mae: 5.4729 - lr: 0.3162\n",
            "Epoch 52/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.5604 - mae: 4.5604 - val_loss: 3.5314 - val_mae: 3.5314 - lr: 0.3548\n",
            "Epoch 53/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.2846 - mae: 4.2846 - val_loss: 8.0393 - val_mae: 8.0393 - lr: 0.3981\n",
            "Epoch 54/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 5.9266 - mae: 5.9266 - val_loss: 5.3955 - val_mae: 5.3955 - lr: 0.4467\n",
            "Epoch 55/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.6319 - mae: 4.6319 - val_loss: 6.7275 - val_mae: 6.7275 - lr: 0.5012\n",
            "Epoch 56/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.3136 - mae: 4.3136 - val_loss: 5.1779 - val_mae: 5.1779 - lr: 0.5623\n",
            "Epoch 57/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.7156 - mae: 3.7156 - val_loss: 3.2240 - val_mae: 3.2240 - lr: 0.6310\n",
            "Epoch 58/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 3.9670 - mae: 3.9670 - val_loss: 5.6938 - val_mae: 5.6938 - lr: 0.7079\n",
            "Epoch 59/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.2640 - mae: 4.2640 - val_loss: 3.9292 - val_mae: 3.9292 - lr: 0.7943\n",
            "Epoch 60/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.8359 - mae: 3.8359 - val_loss: 3.6967 - val_mae: 3.6967 - lr: 0.8913\n",
            "Epoch 61/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.0544 - mae: 4.0544 - val_loss: 3.8827 - val_mae: 3.8827 - lr: 1.0000\n",
            "Epoch 62/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.0956 - mae: 4.0956 - val_loss: 6.3662 - val_mae: 6.3662 - lr: 1.1220\n",
            "Epoch 63/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.0498 - mae: 4.0498 - val_loss: 4.2212 - val_mae: 4.2212 - lr: 1.2589\n",
            "Epoch 64/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.6109 - mae: 4.6109 - val_loss: 3.4927 - val_mae: 3.4927 - lr: 1.4125\n",
            "Epoch 65/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.0197 - mae: 4.0197 - val_loss: 3.9586 - val_mae: 3.9586 - lr: 1.5849\n",
            "Epoch 66/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.4906 - mae: 4.4906 - val_loss: 3.9784 - val_mae: 3.9784 - lr: 1.7783\n",
            "Epoch 67/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.9365 - mae: 3.9365 - val_loss: 3.2320 - val_mae: 3.2320 - lr: 1.9953\n",
            "Epoch 68/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 3.9877 - mae: 3.9877 - val_loss: 5.2717 - val_mae: 5.2717 - lr: 2.2387\n",
            "Epoch 69/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 4.8543 - mae: 4.8543 - val_loss: 6.1424 - val_mae: 6.1424 - lr: 2.5119\n",
            "Epoch 70/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 31.5785 - mae: 31.5785 - val_loss: 7.4158 - val_mae: 7.4158 - lr: 2.8184\n",
            "Epoch 71/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.8035 - mae: 6.8035 - val_loss: 18.4839 - val_mae: 18.4839 - lr: 3.1623\n",
            "Epoch 72/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 7.6930 - mae: 7.6930 - val_loss: 7.4604 - val_mae: 7.4604 - lr: 3.5481\n",
            "Epoch 73/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.6819 - mae: 6.6819 - val_loss: 7.3938 - val_mae: 7.3938 - lr: 3.9811\n",
            "Epoch 74/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 6.6553 - mae: 6.6553 - val_loss: 7.6840 - val_mae: 7.6840 - lr: 4.4668\n",
            "Epoch 75/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.6973 - mae: 6.6973 - val_loss: 7.6829 - val_mae: 7.6829 - lr: 5.0119\n",
            "Epoch 76/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.6670 - mae: 6.6670 - val_loss: 7.4025 - val_mae: 7.4025 - lr: 5.6234\n",
            "Epoch 77/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.8046 - mae: 6.8046 - val_loss: 7.4062 - val_mae: 7.4062 - lr: 6.3096\n",
            "Epoch 78/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.6715 - mae: 6.6715 - val_loss: 7.3930 - val_mae: 7.3930 - lr: 7.0795\n",
            "Epoch 79/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.7239 - mae: 6.7239 - val_loss: 7.4260 - val_mae: 7.4260 - lr: 7.9433\n",
            "Epoch 80/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.6317 - mae: 6.6317 - val_loss: 8.2131 - val_mae: 8.2131 - lr: 8.9125\n",
            "Epoch 81/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.8219 - mae: 6.8219 - val_loss: 8.7911 - val_mae: 8.7911 - lr: 10.0000\n",
            "Epoch 82/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 7.0401 - mae: 7.0401 - val_loss: 8.0167 - val_mae: 8.0167 - lr: 11.2202\n",
            "Epoch 83/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.8738 - mae: 6.8738 - val_loss: 7.7741 - val_mae: 7.7741 - lr: 12.5893\n",
            "Epoch 84/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 7.0704 - mae: 7.0704 - val_loss: 7.4262 - val_mae: 7.4262 - lr: 14.1254\n",
            "Epoch 85/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.7975 - mae: 6.7975 - val_loss: 7.4621 - val_mae: 7.4621 - lr: 15.8489\n",
            "Epoch 86/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.8930 - mae: 6.8930 - val_loss: 7.7481 - val_mae: 7.7481 - lr: 17.7828\n",
            "Epoch 87/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 7.7288 - mae: 7.7288 - val_loss: 10.4177 - val_mae: 10.4177 - lr: 19.9526\n",
            "Epoch 88/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 7.3856 - mae: 7.3856 - val_loss: 7.7918 - val_mae: 7.7918 - lr: 22.3872\n",
            "Epoch 89/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 7.0609 - mae: 7.0609 - val_loss: 7.4254 - val_mae: 7.4254 - lr: 25.1189\n",
            "Epoch 90/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.9768 - mae: 6.9768 - val_loss: 7.4261 - val_mae: 7.4261 - lr: 28.1838\n",
            "Epoch 91/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 7.1079 - mae: 7.1079 - val_loss: 7.4129 - val_mae: 7.4129 - lr: 31.6228\n",
            "Epoch 92/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 7.7226 - mae: 7.7226 - val_loss: 7.3987 - val_mae: 7.3987 - lr: 35.4813\n",
            "Epoch 93/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.9003 - mae: 6.9003 - val_loss: 7.3983 - val_mae: 7.3983 - lr: 39.8107\n",
            "Epoch 94/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 6.9550 - mae: 6.9550 - val_loss: 11.2413 - val_mae: 11.2413 - lr: 44.6684\n",
            "Epoch 95/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 8.6788 - mae: 8.6788 - val_loss: 11.0198 - val_mae: 11.0198 - lr: 50.1187\n",
            "Epoch 96/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 9.5987 - mae: 9.5987 - val_loss: 10.2928 - val_mae: 10.2928 - lr: 56.2341\n",
            "Epoch 97/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 9.5898 - mae: 9.5898 - val_loss: 8.4813 - val_mae: 8.4813 - lr: 63.0957\n",
            "Epoch 98/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 12.8591 - mae: 12.8591 - val_loss: 16.9208 - val_mae: 16.9208 - lr: 70.7946\n",
            "Epoch 99/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 16.8209 - mae: 16.8209 - val_loss: 15.4467 - val_mae: 15.4467 - lr: 79.4328\n",
            "Epoch 100/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 9.2687 - mae: 9.2687 - val_loss: 8.6129 - val_mae: 8.6129 - lr: 89.1251\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the loss curves\n",
        "pd.DataFrame(history.history).plot(figsize=(6, 4), xlabel='Epochs', ylabel='Amount', title='Loss Curves')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "1sYAI2ljh9ij",
        "outputId": "74fa1c62-0449-4f16-8169-5956a2f62cc6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "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": [
        "# Plot the learning rate decay curve\n",
        "plt.semilogx(history.history['lr'], history.history[\"loss\"])\n",
        "plt.xlabel(\"Learning rate\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Finding the ideal learning rate\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "NFPUeqSkiCZR",
        "outputId": "6e594018-5294-4fb5-c8cf-29659d491f04"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": "markdown",
      "source": [
        "## Optimized Model"
      ],
      "metadata": {
        "id": "FhP8VP8xHIIj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "learning rate = 0.01,\n",
        "batch_size = 32,\n",
        "dense_layers = 2,\n",
        "hidden_units for Dense_1 layer= 10,\n",
        "hidden_units for Dense_2 layer = 100"
      ],
      "metadata": {
        "id": "wt3qQohTiNK8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Building the model\n",
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(units=10, activation='relu', name='Dense_1'),\n",
        "  tf.keras.layers.Dense(units=100, activation='relu', name='Dense_2'),\n",
        "  tf.keras.layers.Dense(units=1, name='Prediction')\n",
        "])\n",
        "\n",
        "# Compiling the model\n",
        "model.compile(loss = tf.keras.losses.mean_absolute_error,\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate=0.01),\n",
        "              metrics = ['mae'])\n",
        "\n",
        "# Training the model\n",
        "history = model.fit(X_train,\n",
        "                    y_train,\n",
        "                    batch_size=32,\n",
        "                    epochs=50,\n",
        "                    validation_data=(X_val, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxuLioVJHXep",
        "outputId": "db41144b-fcda-4412-d658-e8c7b4b48129"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 19.3459 - mae: 19.3459 - val_loss: 14.9285 - val_mae: 14.9285\n",
            "Epoch 2/50\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 9.7673 - mae: 9.7673 - val_loss: 9.0249 - val_mae: 9.0249\n",
            "Epoch 3/50\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 7.5432 - mae: 7.5432 - val_loss: 7.5667 - val_mae: 7.5667\n",
            "Epoch 4/50\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 5.8400 - mae: 5.8400 - val_loss: 6.3838 - val_mae: 6.3838\n",
            "Epoch 5/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 5.0631 - mae: 5.0631 - val_loss: 5.8580 - val_mae: 5.8580\n",
            "Epoch 6/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 5.0018 - mae: 5.0018 - val_loss: 5.6465 - val_mae: 5.6465\n",
            "Epoch 7/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.7384 - mae: 4.7384 - val_loss: 5.4774 - val_mae: 5.4774\n",
            "Epoch 8/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.4225 - mae: 4.4225 - val_loss: 5.3765 - val_mae: 5.3765\n",
            "Epoch 9/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.1296 - mae: 4.1296 - val_loss: 5.1494 - val_mae: 5.1494\n",
            "Epoch 10/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 4.1072 - mae: 4.1072 - val_loss: 4.4977 - val_mae: 4.4977\n",
            "Epoch 11/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.7602 - mae: 3.7602 - val_loss: 4.1173 - val_mae: 4.1173\n",
            "Epoch 12/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.5168 - mae: 3.5168 - val_loss: 3.8669 - val_mae: 3.8669\n",
            "Epoch 13/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.2583 - mae: 3.2583 - val_loss: 3.6619 - val_mae: 3.6619\n",
            "Epoch 14/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.1028 - mae: 3.1028 - val_loss: 3.6951 - val_mae: 3.6951\n",
            "Epoch 15/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.9992 - mae: 2.9992 - val_loss: 3.4616 - val_mae: 3.4616\n",
            "Epoch 16/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 3.0581 - mae: 3.0581 - val_loss: 3.6160 - val_mae: 3.6160\n",
            "Epoch 17/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.9441 - mae: 2.9441 - val_loss: 3.6600 - val_mae: 3.6600\n",
            "Epoch 18/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.9702 - mae: 2.9702 - val_loss: 3.1660 - val_mae: 3.1660\n",
            "Epoch 19/50\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 2.7903 - mae: 2.7903 - val_loss: 2.8910 - val_mae: 2.8910\n",
            "Epoch 20/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.9157 - mae: 2.9157 - val_loss: 2.8411 - val_mae: 2.8411\n",
            "Epoch 21/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.7517 - mae: 2.7517 - val_loss: 2.8163 - val_mae: 2.8163\n",
            "Epoch 22/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.7308 - mae: 2.7308 - val_loss: 2.8317 - val_mae: 2.8317\n",
            "Epoch 23/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.8013 - mae: 2.8013 - val_loss: 2.7268 - val_mae: 2.7268\n",
            "Epoch 24/50\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 2.7545 - mae: 2.7545 - val_loss: 2.8109 - val_mae: 2.8109\n",
            "Epoch 25/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.8599 - mae: 2.8599 - val_loss: 2.7016 - val_mae: 2.7016\n",
            "Epoch 26/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.7251 - mae: 2.7251 - val_loss: 3.1589 - val_mae: 3.1589\n",
            "Epoch 27/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.6699 - mae: 2.6699 - val_loss: 2.6659 - val_mae: 2.6659\n",
            "Epoch 28/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.6730 - mae: 2.6730 - val_loss: 2.7998 - val_mae: 2.7998\n",
            "Epoch 29/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.7632 - mae: 2.7632 - val_loss: 2.8660 - val_mae: 2.8660\n",
            "Epoch 30/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.8133 - mae: 2.8133 - val_loss: 2.6062 - val_mae: 2.6062\n",
            "Epoch 31/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.5274 - mae: 2.5274 - val_loss: 2.8606 - val_mae: 2.8606\n",
            "Epoch 32/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.5958 - mae: 2.5958 - val_loss: 2.9006 - val_mae: 2.9006\n",
            "Epoch 33/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.6793 - mae: 2.6793 - val_loss: 3.1139 - val_mae: 3.1139\n",
            "Epoch 34/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.5670 - mae: 2.5670 - val_loss: 2.8094 - val_mae: 2.8094\n",
            "Epoch 35/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.6350 - mae: 2.6350 - val_loss: 2.5910 - val_mae: 2.5910\n",
            "Epoch 36/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.5092 - mae: 2.5092 - val_loss: 2.6011 - val_mae: 2.6011\n",
            "Epoch 37/50\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 2.5185 - mae: 2.5185 - val_loss: 2.7039 - val_mae: 2.7039\n",
            "Epoch 38/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.5060 - mae: 2.5060 - val_loss: 2.6125 - val_mae: 2.6125\n",
            "Epoch 39/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.4527 - mae: 2.4527 - val_loss: 2.5094 - val_mae: 2.5094\n",
            "Epoch 40/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.5302 - mae: 2.5302 - val_loss: 2.4666 - val_mae: 2.4666\n",
            "Epoch 41/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.6048 - mae: 2.6048 - val_loss: 2.5539 - val_mae: 2.5539\n",
            "Epoch 42/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.6164 - mae: 2.6164 - val_loss: 2.4736 - val_mae: 2.4736\n",
            "Epoch 43/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.4774 - mae: 2.4774 - val_loss: 2.4214 - val_mae: 2.4214\n",
            "Epoch 44/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.5242 - mae: 2.5242 - val_loss: 2.5314 - val_mae: 2.5314\n",
            "Epoch 45/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.6222 - mae: 2.6222 - val_loss: 2.3542 - val_mae: 2.3542\n",
            "Epoch 46/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.4822 - mae: 2.4822 - val_loss: 2.3592 - val_mae: 2.3592\n",
            "Epoch 47/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.5323 - mae: 2.5323 - val_loss: 2.5049 - val_mae: 2.5049\n",
            "Epoch 48/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.4801 - mae: 2.4801 - val_loss: 2.4385 - val_mae: 2.4385\n",
            "Epoch 49/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.5713 - mae: 2.5713 - val_loss: 2.3780 - val_mae: 2.3780\n",
            "Epoch 50/50\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 2.5383 - mae: 2.5383 - val_loss: 2.4566 - val_mae: 2.4566\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Evaluation"
      ],
      "metadata": {
        "id": "VasQEdJRe9NK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preview the mean value of training and validation data\n",
        "y_train.mean(), y_val.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1t-7HcflKgGV",
        "outputId": "71e123d7-73d3-4d6e-97d9-3a6aa6774076"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22.235537, 24.89756)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test data\n",
        "print(\"Evaluation on Test data \\n\")\n",
        "loss, mae = model.evaluate(X_test, y_test, batch_size=32)\n",
        "print(f\"\\nModel loss on test set: {loss}\")\n",
        "print(f\"Model mean absolute error on test set: {(mae):.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4F5mom8FfACb",
        "outputId": "1577826d-50d7-47a8-ab8c-d423d52be46e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation on Test data \n",
            "\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.5389 - mae: 2.5389\n",
            "\n",
            "Model loss on test set: 2.5389482975006104\n",
            "Model mean absolute error on test set: 2.54\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the loss curves\n",
        "pd.DataFrame(history.history).plot(figsize=(6, 4), xlabel=\"Epochs\", ylabel=\"Loss\", title='Loss Curves')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "1CD_HeZ1LFZ5",
        "outputId": "4caf3820-2be3-421d-9826-600baf83d452"
      },
      "execution_count": 22,
      "outputs": [
        {
          "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": "markdown",
      "source": [
        "## Model Prediction"
      ],
      "metadata": {
        "id": "Wsto_sf8fXM-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# View the first prediction\n",
        "y_pred[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TrKgnaZpfmJI",
        "outputId": "acd18217-d775-4d3a-b901-e490d8628d6f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([18.916084], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    }
  ]
}