01_neural_network_regression_with_tensorflow.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM9uZP+FqQ+47fJC5wJUF4W",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Blurr99/tensorflow_deepLearning/blob/main/01_neural_network_regression_with_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction to Regression with Neural Networks in TensorFlow"
      ],
      "metadata": {
        "id": "6sKrM5EjGvAi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import TensorFlow\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEoNxNiHHDeX",
        "outputId": "aaf109a9-1e5a-4e4f-b43a-830548b1e68e"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.14.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Creating Data to view and fit\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create features\n",
        "X = np.array([-7.0, -4.0, -1.0, 2.0, 5.0, 8.0, 11.0, 14.0])\n",
        "\n",
        "# Create Labels\n",
        "y = np.array([3.0, 6.0, 9.0, 12.0, 15.0, 18.0, 21.0, 24.0])\n",
        "\n",
        "# Visualizing it\n",
        "plt.scatter(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "yPHJHT3KHPMM",
        "outputId": "e3e9fe71-fe1a-4316-d843-1294dbf7dcc6"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7e42ff45aef0>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Input and output shapes"
      ],
      "metadata": {
        "id": "ZfZgi4p3HvMB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a demo tensor for our housing price prediction problem\n",
        "house_info = tf.constant([\"bedroom\",\"bathroom\",\"garage\"])\n",
        "house_price = tf.constant([939700])\n",
        "house_info, house_price"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b2CSNwnfIA6O",
        "outputId": "6d4894ed-d219-417e-f500-7211da3a8261"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(3,), dtype=string, numpy=array([b'bedroom', b'bathroom', b'garage'], dtype=object)>,\n",
              " <tf.Tensor: shape=(1,), dtype=int32, numpy=array([939700], dtype=int32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_shape = X.shape,\n",
        "output_shape = y.shape\n",
        "input_shape, output_shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H-lvKKGYIbcu",
        "outputId": "ac0e40e9-bf13-40ce-e39f-6367840b6890"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(((8,),), (8,))"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = tf.cast(tf.constant(X), dtype = tf.float32)\n",
        "y = tf.cast(tf.constant(y), dtype = tf.float32)"
      ],
      "metadata": {
        "id": "wf-n30QIIm-s"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_shape = X[0].shape\n",
        "output_shape = y[0].shape"
      ],
      "metadata": {
        "id": "w1ftH-VKJiIq"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_shape, output_shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-FWxE87-Jmyj",
        "outputId": "bb69425b-dbbf-4c6d-a3a3-34ed17d90459"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorShape([]), TensorShape([]))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Steps in modelling with TensorFlow\n",
        "\n",
        "1. **Creating a model**- defining the input and output layers, as well as the hidden layersof a deep learing model.\n",
        "2. **Compiling the model**- define the loss function and the optimizer and evaluating metrics.\n",
        "3. **Fitting the model**- letting the model find patterns between x and y"
      ],
      "metadata": {
        "id": "crD05visJoh5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "#1. Create a model with Sequential API\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "#2. Compile the model\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.SGD(),\n",
        "              metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model.fit(tf.expand_dims(X, axis = 1), y, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CLL36dsJsqr",
        "outputId": "dd40b440-d3a8-4a9a-9ffb-39b9ad4cdfed"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1/1 [==============================] - 0s 457ms/step - loss: 12.8836 - mae: 12.8836\n",
            "Epoch 2/5\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 12.7511 - mae: 12.7511\n",
            "Epoch 3/5\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 12.6186 - mae: 12.6186\n",
            "Epoch 4/5\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 12.4861 - mae: 12.4861\n",
            "Epoch 5/5\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 12.3536 - mae: 12.3536\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e42fc0727d0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y"
      ],
      "metadata": {
        "id": "DHEzverZMOFj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa0987fa-d15a-43b8-eb5c-62b11d6f3f84"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(8,), dtype=float32, numpy=array([-7., -4., -1.,  2.,  5.,  8., 11., 14.], dtype=float32)>,\n",
              " <tf.Tensor: shape=(8,), dtype=float32, numpy=array([ 3.,  6.,  9., 12., 15., 18., 21., 24.], dtype=float32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([17.0])"
      ],
      "metadata": {
        "id": "Ng0EovJ3Mo8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8530e751-3636-4954-fd24-56ab22374457"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 92ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.0190325]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Improving our model\n",
        "\n",
        "The model can be improved by altering the steps taken to create the model:\n",
        "\n",
        "1. **Creating a model**- Adding more layers, increase the number of hidden neurons, changing the activation function of each layer etc.\n",
        "\n",
        "2. **Compiling a model**- Changing the optimization function, **learning rate** of the said function.\n",
        "\n",
        "3. **Fitting a model**- Increasing the number of epochs or the data."
      ],
      "metadata": {
        "id": "E4wzdfuQ7_uW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Rebuilding the model\n",
        "\n",
        "#1. Creating the model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compiling the model\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.SGD(),\n",
        "              metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fitting the model\n",
        "model.fit(tf.expand_dims(X, axis = -1), y, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOHGpgYl8StE",
        "outputId": "b20fad21-ca6d-4776-fefb-f76bc3f9ce34"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 0s 333ms/step - loss: 19.7897 - mae: 19.7897\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 19.5085 - mae: 19.5085\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 19.2272 - mae: 19.2272\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 18.9460 - mae: 18.9460\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 18.6647 - mae: 18.6647\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 18.3835 - mae: 18.3835\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 18.1022 - mae: 18.1022\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 17.8210 - mae: 17.8210\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 17.5397 - mae: 17.5397\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 17.2585 - mae: 17.2585\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 16.9772 - mae: 16.9772\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 16.6960 - mae: 16.6960\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 16.4147 - mae: 16.4147\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 16.1335 - mae: 16.1335\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 15.8522 - mae: 15.8522\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 15.5710 - mae: 15.5710\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 15.2897 - mae: 15.2897\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 15.0085 - mae: 15.0085\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 14.7507 - mae: 14.7507\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 14.6182 - mae: 14.6182\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 14.4857 - mae: 14.4857\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 14.3532 - mae: 14.3532\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 14.2207 - mae: 14.2207\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 14.0882 - mae: 14.0882\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 13.9557 - mae: 13.9557\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 13.8232 - mae: 13.8232\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 13.6907 - mae: 13.6907\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 13.5582 - mae: 13.5582\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 13.4257 - mae: 13.4257\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 13.2932 - mae: 13.2932\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 13.1607 - mae: 13.1607\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 13.0282 - mae: 13.0282\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 12.8957 - mae: 12.8957\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 12.7632 - mae: 12.7632\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 12.6307 - mae: 12.6307\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 12.4982 - mae: 12.4982\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 12.3657 - mae: 12.3657\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 12.2332 - mae: 12.2332\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 12.1007 - mae: 12.1007\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 11.9682 - mae: 11.9682\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 11.8357 - mae: 11.8357\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 11.7032 - mae: 11.7032\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 11.5707 - mae: 11.5707\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 11.4382 - mae: 11.4382\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 11.3057 - mae: 11.3057\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 11.1732 - mae: 11.1732\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 11.0407 - mae: 11.0407\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 10.9082 - mae: 10.9082\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.7757 - mae: 10.7757\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.6432 - mae: 10.6432\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.5107 - mae: 10.5107\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.3782 - mae: 10.3782\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.2457 - mae: 10.2457\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 10.1132 - mae: 10.1132\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.9807 - mae: 9.9807\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 9.8482 - mae: 9.8482\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.7157 - mae: 9.7157\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 9.5832 - mae: 9.5832\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.4507 - mae: 9.4507\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.3182 - mae: 9.3182\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.1857 - mae: 9.1857\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.0532 - mae: 9.0532\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 8.9207 - mae: 8.9207\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 8.7882 - mae: 8.7882\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 8.6557 - mae: 8.6557\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 8.5232 - mae: 8.5232\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 8.3907 - mae: 8.3907\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 8.2582 - mae: 8.2582\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 8.1257 - mae: 8.1257\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.9932 - mae: 7.9932\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.8607 - mae: 7.8607\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.7282 - mae: 7.7282\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.5957 - mae: 7.5957\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.4632 - mae: 7.4632\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.3307 - mae: 7.3307\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.1982 - mae: 7.1982\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.0657 - mae: 7.0657\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9563 - mae: 6.9563\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9506 - mae: 6.9506\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9450 - mae: 6.9450\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9394 - mae: 6.9394\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9338 - mae: 6.9338\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9281 - mae: 6.9281\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9225 - mae: 6.9225\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9169 - mae: 6.9169\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9113 - mae: 6.9113\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.9056 - mae: 6.9056\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 6.9000 - mae: 6.9000\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 6.8944 - mae: 6.8944\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.8888 - mae: 6.8888\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.8831 - mae: 6.8831\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.8775 - mae: 6.8775\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.8719 - mae: 6.8719\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.8663 - mae: 6.8663\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.8606 - mae: 6.8606\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.8550 - mae: 6.8550\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.8494 - mae: 6.8494\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.8438 - mae: 6.8438\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.8381 - mae: 6.8381\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.8325 - mae: 6.8325\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e42f56cda20>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9O_3wlQy_MMG",
        "outputId": "b22683bb-06e9-476a-b2a0-bfb76b66b8e8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(8,), dtype=float32, numpy=array([-7., -4., -1.,  2.,  5.,  8., 11., 14.], dtype=float32)>,\n",
              " <tf.Tensor: shape=(8,), dtype=float32, numpy=array([ 3.,  6.,  9., 12., 15., 18., 21., 24.], dtype=float32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the model's prediction\n",
        "model.predict([17.])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RrwdovVy_zw-",
        "outputId": "8b7ef2c6-8812-41f2-82cc-be4484fd88ef"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 60ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[29.272121]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1. Creating the model (with another hidden layer and a 100 neurons)\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(50),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compiling the model\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.Adam(lr = 0.001),\n",
        "              metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fitting the model\n",
        "model.fit(tf.expand_dims(X, axis = -1), y, epochs = 90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "76ndW47B_4md",
        "outputId": "079c1eac-d48e-4252-84cb-779f31a60665"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/90\n",
            "1/1 [==============================] - 1s 598ms/step - loss: 12.7731 - mae: 12.7731\n",
            "Epoch 2/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 12.7035 - mae: 12.7035\n",
            "Epoch 3/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 12.6340 - mae: 12.6340\n",
            "Epoch 4/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 12.5644 - mae: 12.5644\n",
            "Epoch 5/90\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 12.4947 - mae: 12.4947\n",
            "Epoch 6/90\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 12.4251 - mae: 12.4251\n",
            "Epoch 7/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 12.3554 - mae: 12.3554\n",
            "Epoch 8/90\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 12.2858 - mae: 12.2858\n",
            "Epoch 9/90\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 12.2160 - mae: 12.2160\n",
            "Epoch 10/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 12.1463 - mae: 12.1463\n",
            "Epoch 11/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 12.0765 - mae: 12.0765\n",
            "Epoch 12/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 12.0067 - mae: 12.0067\n",
            "Epoch 13/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 11.9368 - mae: 11.9368\n",
            "Epoch 14/90\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 11.8669 - mae: 11.8669\n",
            "Epoch 15/90\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 11.7969 - mae: 11.7969\n",
            "Epoch 16/90\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 11.7269 - mae: 11.7269\n",
            "Epoch 17/90\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 11.6567 - mae: 11.6567\n",
            "Epoch 18/90\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 11.5865 - mae: 11.5865\n",
            "Epoch 19/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 11.5162 - mae: 11.5162\n",
            "Epoch 20/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 11.4458 - mae: 11.4458\n",
            "Epoch 21/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 11.3754 - mae: 11.3754\n",
            "Epoch 22/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 11.3048 - mae: 11.3048\n",
            "Epoch 23/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 11.2341 - mae: 11.2341\n",
            "Epoch 24/90\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 11.1633 - mae: 11.1633\n",
            "Epoch 25/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 11.0924 - mae: 11.0924\n",
            "Epoch 26/90\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 11.0213 - mae: 11.0213\n",
            "Epoch 27/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.9501 - mae: 10.9501\n",
            "Epoch 28/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.8788 - mae: 10.8788\n",
            "Epoch 29/90\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 10.8074 - mae: 10.8074\n",
            "Epoch 30/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.7358 - mae: 10.7358\n",
            "Epoch 31/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.6640 - mae: 10.6640\n",
            "Epoch 32/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.5921 - mae: 10.5921\n",
            "Epoch 33/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.5200 - mae: 10.5200\n",
            "Epoch 34/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.4477 - mae: 10.4477\n",
            "Epoch 35/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.3753 - mae: 10.3753\n",
            "Epoch 36/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.3026 - mae: 10.3026\n",
            "Epoch 37/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 10.2298 - mae: 10.2298\n",
            "Epoch 38/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.1568 - mae: 10.1568\n",
            "Epoch 39/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.0836 - mae: 10.0836\n",
            "Epoch 40/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 10.0101 - mae: 10.0101\n",
            "Epoch 41/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.9365 - mae: 9.9365\n",
            "Epoch 42/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.8626 - mae: 9.8626\n",
            "Epoch 43/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.7885 - mae: 9.7885\n",
            "Epoch 44/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.7142 - mae: 9.7142\n",
            "Epoch 45/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.6396 - mae: 9.6396\n",
            "Epoch 46/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.5647 - mae: 9.5647\n",
            "Epoch 47/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.4897 - mae: 9.4897\n",
            "Epoch 48/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.4143 - mae: 9.4143\n",
            "Epoch 49/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.3387 - mae: 9.3387\n",
            "Epoch 50/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.2628 - mae: 9.2628\n",
            "Epoch 51/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.1866 - mae: 9.1866\n",
            "Epoch 52/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.1102 - mae: 9.1102\n",
            "Epoch 53/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.0334 - mae: 9.0334\n",
            "Epoch 54/90\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 8.9564 - mae: 8.9564\n",
            "Epoch 55/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 8.8790 - mae: 8.8790\n",
            "Epoch 56/90\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.8014 - mae: 8.8014\n",
            "Epoch 57/90\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.7234 - mae: 8.7234\n",
            "Epoch 58/90\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.6451 - mae: 8.6451\n",
            "Epoch 59/90\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.5665 - mae: 8.5665\n",
            "Epoch 60/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 8.4875 - mae: 8.4875\n",
            "Epoch 61/90\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 8.4082 - mae: 8.4082\n",
            "Epoch 62/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 8.3286 - mae: 8.3286\n",
            "Epoch 63/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 8.2486 - mae: 8.2486\n",
            "Epoch 64/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 8.1683 - mae: 8.1683\n",
            "Epoch 65/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 8.0876 - mae: 8.0876\n",
            "Epoch 66/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 8.0065 - mae: 8.0065\n",
            "Epoch 67/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.9251 - mae: 7.9251\n",
            "Epoch 68/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.8433 - mae: 7.8433\n",
            "Epoch 69/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.7611 - mae: 7.7611\n",
            "Epoch 70/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.6785 - mae: 7.6785\n",
            "Epoch 71/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.5955 - mae: 7.5955\n",
            "Epoch 72/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.5121 - mae: 7.5121\n",
            "Epoch 73/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.4283 - mae: 7.4283\n",
            "Epoch 74/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.3441 - mae: 7.3441\n",
            "Epoch 75/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.2595 - mae: 7.2595\n",
            "Epoch 76/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.1745 - mae: 7.1745\n",
            "Epoch 77/90\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 7.0891 - mae: 7.0891\n",
            "Epoch 78/90\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 7.0032 - mae: 7.0032\n",
            "Epoch 79/90\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 6.9169 - mae: 6.9169\n",
            "Epoch 80/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.8301 - mae: 6.8301\n",
            "Epoch 81/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.8091 - mae: 6.8091\n",
            "Epoch 82/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.7985 - mae: 6.7985\n",
            "Epoch 83/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.7882 - mae: 6.7882\n",
            "Epoch 84/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.7781 - mae: 6.7781\n",
            "Epoch 85/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.7681 - mae: 6.7681\n",
            "Epoch 86/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.7584 - mae: 6.7584\n",
            "Epoch 87/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.7488 - mae: 6.7488\n",
            "Epoch 88/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.7393 - mae: 6.7393\n",
            "Epoch 89/90\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.7299 - mae: 6.7299\n",
            "Epoch 90/90\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 6.7207 - mae: 6.7207\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e42f4d59960>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bOgyAPeBAVeJ",
        "outputId": "fab782ea-5b9f-4ce5-e1ec-e0e24b483a8f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(8,), dtype=float32, numpy=array([-7., -4., -1.,  2.,  5.,  8., 11., 14.], dtype=float32)>,\n",
              " <tf.Tensor: shape=(8,), dtype=float32, numpy=array([ 3.,  6.,  9., 12., 15., 18., 21., 24.], dtype=float32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Trying to make a prediction again\n",
        "model.predict([17.])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCo2BbHyBD4y",
        "outputId": "5005cad7-6502-4e63-a041-d2f397a107a6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 71ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[31.59443]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating the model\n",
        "\n",
        "To evaluate a model, its a good idea to visualize:\n",
        "* The data - What does the data look like?\n",
        "* The model - What does the model look like?\n",
        "* The training of the model - How does the model perform while it learns\n",
        "* The predictions of the model - How do the predictions of a model line up against the ground truth?"
      ],
      "metadata": {
        "id": "XJ5oQUIwBGw_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a bigger dataset\n",
        "X = tf.range(-100, 100, 4)\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBzPhYGkCwCU",
        "outputId": "b57e933b-d930-418b-c1df-f63012f6f932"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(50,), dtype=int32, numpy=\n",
              "array([-100,  -96,  -92,  -88,  -84,  -80,  -76,  -72,  -68,  -64,  -60,\n",
              "        -56,  -52,  -48,  -44,  -40,  -36,  -32,  -28,  -24,  -20,  -16,\n",
              "        -12,   -8,   -4,    0,    4,    8,   12,   16,   20,   24,   28,\n",
              "         32,   36,   40,   44,   48,   52,   56,   60,   64,   68,   72,\n",
              "         76,   80,   84,   88,   92,   96], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make labels for the dataset\n",
        "y = X + 10\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WqO4DfXFD2o6",
        "outputId": "1c0692a9-4f62-4446-9dd0-2b218f8e5dad"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(50,), dtype=int32, numpy=\n",
              "array([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42,\n",
              "       -38, -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,\n",
              "        14,  18,  22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,\n",
              "        66,  70,  74,  78,  82,  86,  90,  94,  98, 102, 106], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the data\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Fc4H8bxJD6Yi",
        "outputId": "bf780f8d-e7ea-4926-a794-bff9508f06d0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7e42f4d66dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "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": [
        "X_train = X[:40]\n",
        "X_test = X[40:]\n",
        "\n",
        "y_train = y[:40]\n",
        "y_test = y[40:]\n",
        "\n",
        "len(X_train), len(X_test), len(y_train), len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5SL7q3kGEAY9",
        "outputId": "a3b0a63a-1ab3-4968-e13a-0f21d03e0b21"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 10, 40, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualize the data"
      ],
      "metadata": {
        "id": "1i7c1_9iEWnA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10, 7))\n",
        "# Plotting the training data in blue\n",
        "plt.scatter(X_train, y_train, c = \"b\", label = \"Training Data\")\n",
        "\n",
        "# Plotting the testing data in green\n",
        "plt.scatter(X_test, y_test, c = \"g\", label = \"Testing Data\")\n",
        "\n",
        "# Show a legend\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "9JOxRYMjFSPP",
        "outputId": "46969554-8132-4bc4-8b88-da86aa5f3903"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building a neural network for the new data\n",
        "\n",
        "# 1. Create a model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.SGD(),\n",
        "              metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fitting the model\n",
        "#model.fit(X_train, y_train, epochs = 100)"
      ],
      "metadata": {
        "id": "cpDC0iciF5-T"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X[0], y[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFym62QcJ62Q",
        "outputId": "9423ab20-a471-447a-90ca-5e081bc6eaa7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(), dtype=int32, numpy=-100>,\n",
              " <tf.Tensor: shape=(), dtype=int32, numpy=-90>)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a model that builds automatically by defining the input_shape argument\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Creating a model same as above\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(10, input_shape = [1], name = \"input_layer\"),\n",
        "    tf.keras.layers.Dense(1, name = \"output_layer\")\n",
        "], name = \"a_model\")\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.SGD(),\n",
        "              metrics = [\"mae\"])"
      ],
      "metadata": {
        "id": "r3ZyvZaXHl1P"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otT1_UxJKH4-",
        "outputId": "48fc36c7-b51e-4e88-ac66-ee28f4bc1292"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"a_model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (None, 10)                20        \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 31 (124.00 Byte)\n",
            "Trainable params: 31 (124.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Total params - total number of parameters\n",
        "* Trainable params - the parameters the model can update as it trains\n",
        "* Non-trainable params - parameters that arent updated when training. Typical when bringing in already learnt patterns or parameters from other models during transfer learning"
      ],
      "metadata": {
        "id": "tBSrFM-JKJjw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, epochs = 100, verbose = 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtPcdgH_K-m6",
        "outputId": "b7f60268-252c-4380-ef81-98c92f4b4a86"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e42f46bace0>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffBtRsA0MWU1",
        "outputId": "432e5f26-5fee-4de5-a950-c4a15ad125c5"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"a_model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (Dense)         (None, 10)                20        \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 31 (124.00 Byte)\n",
            "Trainable params: 31 (124.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "plot_model(model=model, show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "LEh_WtgYMrvL",
        "outputId": "d2af743b-f6a6-44e6-b004-cf9f367d326f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualizing the model's predictions\n",
        "\n",
        "To visualize predictions, its a good idea to plot them against the ground truth labels."
      ],
      "metadata": {
        "id": "zLGB2xSZNJPB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make some predictions\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9HoYfJwCOP5H",
        "outputId": "afe372ab-e13f-4e86-85d4-7514a1f2cce7"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 51ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 90.99371 ],\n",
              "       [ 96.936104],\n",
              "       [102.87851 ],\n",
              "       [108.82091 ],\n",
              "       [114.7633  ],\n",
              "       [120.705696],\n",
              "       [126.648094],\n",
              "       [132.5905  ],\n",
              "       [138.5329  ],\n",
              "       [144.4753  ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lyDBrnO3OXFG",
        "outputId": "f36a189b-0363-4827-9e6e-7dbd047430c9"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a plotting function\n",
        "def plot_predictions(train_data = X_train,\n",
        "                     train_labels = y_train,\n",
        "                     test_data = X_test,\n",
        "                     test_labels = y_test,\n",
        "                     predictions = y_pred):\n",
        "  \"\"\"\n",
        "    Plots training data, test data and compares predictions to ground truth labels.\n",
        "  \"\"\"\n",
        "\n",
        "  plt.figure(figsize = (10,7))\n",
        "  # Plot the training data in blue\n",
        "  plt.scatter(train_data, train_labels, c = \"b\", label = \"Training data\")\n",
        "  # Plot the testing data in green\n",
        "  plt.scatter(test_data, test_labels, c = \"g\", label = \"Testing data\")\n",
        "  # Plot model's predictions in red\n",
        "  plt.scatter(test_data, predictions, c=\"r\", label = \"Predictions\")\n",
        "  # Show the legend\n",
        "  plt.legend();"
      ],
      "metadata": {
        "id": "2iGRcUBiOZhe"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "h_hW1Kn8PajA",
        "outputId": "f9173be6-3f9f-464c-8cbd-1780a4a07caa"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluating the model's predictions with regression evaluation metrics\n",
        "\n",
        "Depending on the problem, there will be different evaluation metrics to evaluate the model's performance.\n",
        "\n",
        "For a regression problem, Two of the main metrices are:\n",
        "* MAE - Mean Absolute Error, how wrong is each of the model's prediction (Great Starter metric)\n",
        "* MSE - Mean Square Error (When larger errors are more significant than smaller errors)\n",
        "* Huber - Combination of MSE and MAE. Less sensitive to outliers than MSE"
      ],
      "metadata": {
        "id": "3C7jA5SSPcyq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on test set\n",
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VAlo58mgPxDn",
        "outputId": "e42bab11-6a1e-4736-8166-f7895de0f9e4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 330ms/step - loss: 29.7345 - mae: 29.7345\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[29.734500885009766, 29.734500885009766]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the Mean Absolute Error\n",
        "tf.metrics.mean_absolute_error(y_test, tf.constant(y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8C7WXuCSx2o",
        "outputId": "a8894529-aa5f-4180-9e5b-4480860a0ce6"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([10.198743, 11.974442, 15.502808, 20.820908, 26.7633  , 32.705696,\n",
              "       38.648094, 44.5905  , 50.532898, 56.475292], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.constant(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6auVSxkTIHf",
        "outputId": "28b74322-671a-42df-e742-0ad4687543c1"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 1), dtype=float32, numpy=\n",
              "array([[ 90.99371 ],\n",
              "       [ 96.936104],\n",
              "       [102.87851 ],\n",
              "       [108.82091 ],\n",
              "       [114.7633  ],\n",
              "       [120.705696],\n",
              "       [126.648094],\n",
              "       [132.5905  ],\n",
              "       [138.5329  ],\n",
              "       [144.4753  ]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MpBRqg3QTtO_",
        "outputId": "55ceb6ad-1a6b-4921-98d2-a514224449cd"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.squeeze(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqQBMyyVTt8I",
        "outputId": "86368417-8d31-453c-aac4-aecde4064fdd"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([ 90.99371 ,  96.936104, 102.87851 , 108.82091 , 114.7633  ,\n",
              "       120.705696, 126.648094, 132.5905  , 138.5329  , 144.4753  ],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the Mean Absolute Error\n",
        "tf.metrics.mean_absolute_error(y_test, tf.squeeze(y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-3StuRqTwWu",
        "outputId": "a54d9e3b-bf77-4b5e-97a3-f007f336f364"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=29.734503>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the Mean Absolute Error by expanding the test vector (doesnt work as intended)\n",
        "tf.metrics.mean_absolute_error(tf.expand_dims(y_test, axis = -1), y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vt6Bx5GCT0Dc",
        "outputId": "4187425b-d541-443e-da94-be8b0bffb75e"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([20.993713, 22.936104, 24.87851 , 26.820908, 28.763298, 30.705696,\n",
              "       32.648094, 34.5905  , 36.532898, 38.475296], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.expand_dims(y_test, axis = -1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdKFZBOQUFPZ",
        "outputId": "7cb75e67-9417-40d4-dcdb-ef117ddb4e85"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 1), dtype=int32, numpy=\n",
              "array([[ 70],\n",
              "       [ 74],\n",
              "       [ 78],\n",
              "       [ 82],\n",
              "       [ 86],\n",
              "       [ 90],\n",
              "       [ 94],\n",
              "       [ 98],\n",
              "       [102],\n",
              "       [106]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the Mean Squared Error\n",
        "tf.metrics.mean_squared_error(y_test, tf.squeeze(y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxbOnEWZUHvi",
        "outputId": "0bd95ccb-058e-497c-aeca-074dd2471056"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=915.2672>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making functions to resuse MSE and MAE\n",
        "def mae(y_true, y_pred):\n",
        "  return tf.metrics.mean_absolute_error(y_true, tf.squeeze(y_pred)).numpy()\n",
        "\n",
        "def mse(y_true, y_pred):\n",
        "  return tf.metrics.mean_squared_error(y_true, tf.squeeze(y_pred)).numpy()"
      ],
      "metadata": {
        "id": "I2AQqQAZUhMw"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Running experiments to improve the model\n",
        "\n",
        "1. Get more data- get more examples for the model to train on.\n",
        "2. Make the model larger- this might come in the form of more layers or more hidden units in each layer.\n",
        "3. Train for longer- give the model more of a chance to find patterns in the data"
      ],
      "metadata": {
        "id": "EOxYqXDUUePl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b4EgU_bwVM24",
        "outputId": "e6aa8cdc-ba52-474d-8940-98f94f8521d0"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(40,), dtype=int32, numpy=\n",
              " array([-100,  -96,  -92,  -88,  -84,  -80,  -76,  -72,  -68,  -64,  -60,\n",
              "         -56,  -52,  -48,  -44,  -40,  -36,  -32,  -28,  -24,  -20,  -16,\n",
              "         -12,   -8,   -4,    0,    4,    8,   12,   16,   20,   24,   28,\n",
              "          32,   36,   40,   44,   48,   52,   56], dtype=int32)>,\n",
              " <tf.Tensor: shape=(40,), dtype=int32, numpy=\n",
              " array([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42,\n",
              "        -38, -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,\n",
              "         14,  18,  22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,\n",
              "         66], dtype=int32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trying 3 modelling experiments:\n",
        "  1. `model_1` - same as the original model, 1 layer, trained for 100 epochs.\n",
        "  2. `model_2` - 2 layers, trained for a 100 epochs.\n",
        "  3. `model_3` - 2 layers, trained for 500 epochs.\n",
        "\n",
        "**Build `model_1`**"
      ],
      "metadata": {
        "id": "XTHLGaLgVzVP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Creating the model\n",
        "model_1 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_1.compile(loss = tf.keras.losses.mae,\n",
        "              optimizer = tf.keras.optimizers.SGD(),\n",
        "              metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "model_1.fit(tf.expand_dims(X_train, axis = -1), y_train, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qx0S6B-7WVrr",
        "outputId": "5ead2d25-4cb1-4f9e-de1e-af334f6b37cd"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "2/2 [==============================] - 1s 36ms/step - loss: 99.2737 - mae: 99.2737\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 63.8182 - mae: 63.8182\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 26.6584 - mae: 26.6584\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 11.2425 - mae: 11.2425\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 12.4627 - mae: 12.4627\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 9.6459 - mae: 9.6459\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 8.7546 - mae: 8.7546\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 9.0873 - mae: 9.0873\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 19.1588 - mae: 19.1588\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 10.3613 - mae: 10.3613\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 8.5117 - mae: 8.5117\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 10.8673 - mae: 10.8673\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 9.9170 - mae: 9.9170\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 12.1247 - mae: 12.1247\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 12.4065 - mae: 12.4065\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 8.4504 - mae: 8.4504\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 13.4004 - mae: 13.4004\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 11.1632 - mae: 11.1632\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 18.4277 - mae: 18.4277\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 15.1300 - mae: 15.1300\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 11.0955 - mae: 11.0955\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 8.2279 - mae: 8.2279\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 9.5064 - mae: 9.5064\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 7.7008 - mae: 7.7008\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 13.1193 - mae: 13.1193\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 16.5006 - mae: 16.5006\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 13.2369 - mae: 13.2369\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 14.3417 - mae: 14.3417\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 10.1108 - mae: 10.1108\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 16.4528 - mae: 16.4528\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 23.5633 - mae: 23.5633\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 7.6355 - mae: 7.6355\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 9.3305 - mae: 9.3305\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 13.6890 - mae: 13.6890\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 11.1916 - mae: 11.1916\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 13.4165 - mae: 13.4165\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 9.4743 - mae: 9.4743\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 10.1261 - mae: 10.1261\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 29ms/step - loss: 9.0016 - mae: 9.0016\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 9.6310 - mae: 9.6310\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 10.5912 - mae: 10.5912\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 10.6455 - mae: 10.6455\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 7.2377 - mae: 7.2377\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 35ms/step - loss: 8.0420 - mae: 8.0420\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 9.8459 - mae: 9.8459\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 8.9252 - mae: 8.9252\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 7.5925 - mae: 7.5925\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 8.5549 - mae: 8.5549\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 10.0580 - mae: 10.0580\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 9.0005 - mae: 9.0005\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 10.7352 - mae: 10.7352\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 15.2794 - mae: 15.2794\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 14.3081 - mae: 14.3081\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 21.5519 - mae: 21.5519\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 16.0653 - mae: 16.0653\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 10.2614 - mae: 10.2614\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 9.8221 - mae: 9.8221\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 9.1049 - mae: 9.1049\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 8.3058 - mae: 8.3058\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 9.4007 - mae: 9.4007\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 11.2313 - mae: 11.2313\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 11.9665 - mae: 11.9665\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 7.9193 - mae: 7.9193\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 14.2751 - mae: 14.2751\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 10.5908 - mae: 10.5908\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 11.2772 - mae: 11.2772\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 7.0797 - mae: 7.0797\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 8.9184 - mae: 8.9184\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 10.4871 - mae: 10.4871\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 7.8759 - mae: 7.8759\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 11.3884 - mae: 11.3884\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 9.5759 - mae: 9.5759\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 11.6857 - mae: 11.6857\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 10.7466 - mae: 10.7466\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 9.5747 - mae: 9.5747\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 11.0240 - mae: 11.0240\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 11.0953 - mae: 11.0953\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 11.5908 - mae: 11.5908\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 15.6348 - mae: 15.6348\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 11.5932 - mae: 11.5932\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 9.4938 - mae: 9.4938\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 12.5797 - mae: 12.5797\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 8.1824 - mae: 8.1824\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 7.5610 - mae: 7.5610\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 9.7464 - mae: 9.7464\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 9.0547 - mae: 9.0547\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 11.6865 - mae: 11.6865\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 10.2871 - mae: 10.2871\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 6.7636 - mae: 6.7636\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 8.1252 - mae: 8.1252\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 6.7655 - mae: 6.7655\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 10.2165 - mae: 10.2165\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 7.5893 - mae: 7.5893\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 9.2184 - mae: 9.2184\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 10.7715 - mae: 10.7715\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 10.3893 - mae: 10.3893\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 27ms/step - loss: 7.6471 - mae: 7.6471\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.5904 - mae: 8.5904\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 9.3620 - mae: 9.3620\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 8.8258 - mae: 8.8258\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e42f5e63cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make and plot predictions for model_1\n",
        "y_preds_1 = model_1.predict(X_test)\n",
        "plot_predictions(predictions = y_preds_1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "1_H5Ds8QW0Ye",
        "outputId": "fbbf9b9c-3934-45e9-f163-229af4442364"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7e42f5fd5cf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 110ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate model_1 evaluation metrics\n",
        "mae_1 = mae(y_test, y_preds_1)\n",
        "mse_1 = mse(y_test, y_preds_1)\n",
        "\n",
        "mae_1, mse_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bcacw5y0Xcwk",
        "outputId": "2ed85f61-c4d2-4e5a-b416-873c04a3fe34"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8.325836, 75.62362)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "** Build `model_2`**\n",
        "\n",
        "* 2 layers, trained for 100 epochs"
      ],
      "metadata": {
        "id": "2Iq1LGS6X2sA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Building the model\n",
        "model_2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_2.compile(loss = tf.keras.losses.mae,\n",
        "                optimizer = tf.keras.optimizers.Adam(lr = 0.0001),\n",
        "                metrics = [\"mse\"])\n",
        "\n",
        "# 3. Fitting the model\n",
        "model_2.fit(tf.expand_dims(X_train, axis = -1), y_train, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RH8FV0lZYXJD",
        "outputId": "81b799a5-7c22-42ed-8932-267e39422f44"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "2/2 [==============================] - 1s 29ms/step - loss: 79.1612 - mse: 8671.9814\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 78.6204 - mse: 8553.6514\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 31ms/step - loss: 78.1007 - mse: 8439.3281\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 77.5751 - mse: 8324.7012\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 77.0614 - mse: 8215.3408\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 76.5454 - mse: 8103.6807\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 76.0274 - mse: 7991.7939\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 75.4923 - mse: 7874.1616\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 74.9976 - mse: 7773.8408\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 74.4995 - mse: 7669.8594\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 73.9695 - mse: 7559.1704\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 73.4688 - mse: 7456.4648\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 72.9484 - mse: 7347.6982\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 72.4493 - mse: 7247.6836\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 71.9543 - mse: 7148.8906\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 71.4386 - mse: 7046.4287\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 70.9356 - mse: 6946.0298\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 70.4365 - mse: 6846.2407\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 69.9481 - mse: 6749.9819\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 69.4420 - mse: 6652.1006\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 68.9453 - mse: 6556.7642\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 68.4675 - mse: 6465.6504\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 67.9728 - mse: 6371.7217\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 67.4716 - mse: 6274.7080\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 66.9786 - mse: 6183.3301\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 66.4937 - mse: 6092.7837\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 65.9822 - mse: 5998.2393\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 65.4925 - mse: 5910.5029\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 64.9576 - mse: 5809.9561\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 64.4315 - mse: 5713.9072\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 63.9387 - mse: 5631.0703\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 63.4002 - mse: 5533.9663\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 62.8679 - mse: 5439.1104\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 62.3490 - mse: 5351.6445\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 61.8080 - mse: 5257.1343\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 61.3036 - mse: 5170.5225\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 60.7684 - mse: 5079.3760\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 60.2463 - mse: 4990.6768\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 59.7237 - mse: 4903.8433\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 59.2007 - mse: 4816.2241\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 58.6694 - mse: 4731.1680\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 58.1500 - mse: 4646.0762\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 57.5866 - mse: 4553.7583\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 57.0670 - mse: 4474.1704\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 56.5186 - mse: 4387.6118\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 55.9915 - mse: 4305.9072\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 55.4232 - mse: 4217.1626\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 54.8842 - mse: 4135.5879\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 54.3336 - mse: 4052.1101\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 53.7892 - mse: 3970.2390\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 53.2056 - mse: 3880.8413\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 52.6595 - mse: 3803.1965\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 52.0667 - mse: 3714.2598\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 51.4804 - mse: 3629.0073\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 50.9027 - mse: 3547.5142\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 50.2852 - mse: 3459.4368\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 49.6901 - mse: 3377.8813\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 49.0797 - mse: 3292.9839\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 48.4773 - mse: 3213.9863\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 47.8725 - mse: 3129.9097\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 47.2670 - mse: 3050.8821\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 46.6629 - mse: 2972.2925\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 46.0474 - mse: 2892.3953\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 45.4260 - mse: 2812.3599\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 44.7978 - mse: 2733.7654\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 44.1672 - mse: 2657.9141\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 43.5339 - mse: 2580.2222\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 42.8858 - mse: 2501.6562\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 42.2497 - mse: 2428.9939\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 41.5837 - mse: 2349.8081\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 40.9332 - mse: 2276.5630\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 40.2775 - mse: 2203.1167\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 39.5836 - mse: 2125.2590\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 38.9378 - mse: 2058.4565\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 38.2342 - mse: 1983.2996\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 37.5475 - mse: 1910.2014\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 36.8466 - mse: 1841.1960\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 36.1263 - mse: 1766.8363\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 35.4046 - mse: 1695.5117\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 34.7163 - mse: 1630.2125\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 34.0424 - mse: 1566.1537\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 33.2924 - mse: 1496.9434\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 32.6232 - mse: 1436.7858\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 31.8931 - mse: 1369.0146\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 31.2226 - mse: 1312.4473\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 30.5214 - mse: 1251.7744\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 29.7911 - mse: 1191.5192\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 29.0941 - mse: 1136.4537\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 28.3423 - mse: 1077.1293\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 27.6155 - mse: 1023.9608\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 26.8536 - mse: 967.0519\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 26.1327 - mse: 916.4545\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 43ms/step - loss: 25.3904 - mse: 863.4546\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 24.6318 - mse: 811.9924\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 23.9024 - mse: 764.6692\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 23.1346 - mse: 713.6354\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 22.4062 - mse: 670.3407\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 21.6320 - mse: 624.6187\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 20.8632 - mse: 581.1223\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 20.1301 - mse: 539.6720\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e42f5df4730>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds_2 = model_2.predict(X_test)\n",
        "y_preds_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_8kUm1iZSFX",
        "outputId": "0cdbee13-8685-4eba-d949-2643adedde96"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7e42f5fbd900> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 110ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[30.1747  ],\n",
              "       [32.228794],\n",
              "       [34.282887],\n",
              "       [36.336975],\n",
              "       [38.391068],\n",
              "       [40.44516 ],\n",
              "       [42.49925 ],\n",
              "       [44.553337],\n",
              "       [46.60743 ],\n",
              "       [48.661514]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_predictions(predictions=y_preds_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "W6vwrSYJZABM",
        "outputId": "6fac31ca-df20-4b36-f8a6-a1755c3e5475"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mae_2 = mae(y_test, y_preds_2)\n",
        "mse_2 = mse(y_test, y_preds_2)\n",
        "\n",
        "mae_2, mse_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JXoIDZuhnx6",
        "outputId": "fe4b80aa-e815-4049-934a-d6dc4c39ed74"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48.581886, 2391.439)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Build `model_3`**\n",
        "\n",
        "2 layers, trained for 500 epochs."
      ],
      "metadata": {
        "id": "8xOf4SLSiqm4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Building the model\n",
        "model_3 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "model_3.compile(loss = tf.keras.losses.mae,\n",
        "                optimizer = tf.keras.optimizers.Adam(lr = 0.0001),\n",
        "                metrics = [\"mse\"])\n",
        "\n",
        "# 3. Fitting the model\n",
        "model_3.fit(tf.expand_dims(X_train, axis = -1), y_train, epochs = 500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DbiArt7Tj8JK",
        "outputId": "2d0accdf-b192-4d88-a2bb-dae39cbe30c9"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "2/2 [==============================] - 1s 16ms/step - loss: 51.5548 - mse: 3644.9683\n",
            "Epoch 2/500\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 51.0028 - mse: 3566.8618\n",
            "Epoch 3/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 50.4726 - mse: 3490.9634\n",
            "Epoch 4/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 49.9282 - mse: 3414.8882\n",
            "Epoch 5/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 49.3958 - mse: 3342.2910\n",
            "Epoch 6/500\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 48.8574 - mse: 3268.2446\n",
            "Epoch 7/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 48.3130 - mse: 3193.6033\n",
            "Epoch 8/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 47.7493 - mse: 3115.0938\n",
            "Epoch 9/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 47.2205 - mse: 3047.7415\n",
            "Epoch 10/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 46.6904 - mse: 2978.1997\n",
            "Epoch 11/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 46.1175 - mse: 2904.5598\n",
            "Epoch 12/500\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 45.5784 - mse: 2835.6455\n",
            "Epoch 13/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 45.0095 - mse: 2762.9146\n",
            "Epoch 14/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 44.4664 - mse: 2696.0083\n",
            "Epoch 15/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 43.9222 - mse: 2629.9707\n",
            "Epoch 16/500\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 43.3531 - mse: 2561.8035\n",
            "Epoch 17/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 42.7934 - mse: 2494.7178\n",
            "Epoch 18/500\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 42.2385 - mse: 2429.0813\n",
            "Epoch 19/500\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 41.6927 - mse: 2365.2900\n",
            "Epoch 20/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 41.1245 - mse: 2300.4700\n",
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            "Epoch 179/500\n",
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            "Epoch 180/500\n",
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            "Epoch 181/500\n",
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            "Epoch 189/500\n",
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            "2/2 [==============================] - 0s 12ms/step - loss: 4.9308 - mse: 35.3004\n",
            "Epoch 366/500\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 4.9240 - mse: 35.0869\n",
            "Epoch 367/500\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 4.9174 - mse: 34.8526\n",
            "Epoch 368/500\n",
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            "Epoch 369/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 4.9005 - mse: 34.4393\n",
            "Epoch 370/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 4.8873 - mse: 34.3769\n",
            "Epoch 371/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 4.8750 - mse: 34.3877\n",
            "Epoch 372/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 4.8593 - mse: 34.3260\n",
            "Epoch 373/500\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 4.8484 - mse: 34.1835\n",
            "Epoch 374/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 4.8340 - mse: 34.1279\n",
            "Epoch 375/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 4.8250 - mse: 34.2001\n",
            "Epoch 376/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.8180 - mse: 34.3106\n",
            "Epoch 377/500\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 4.8162 - mse: 34.3622\n",
            "Epoch 378/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 4.8076 - mse: 34.2885\n",
            "Epoch 379/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 4.7914 - mse: 34.0333\n",
            "Epoch 380/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.7660 - mse: 33.5350\n",
            "Epoch 381/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.7578 - mse: 32.8601\n",
            "Epoch 382/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.7854 - mse: 32.3627\n",
            "Epoch 383/500\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 4.7871 - mse: 31.9910\n",
            "Epoch 384/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 4.7692 - mse: 31.9321\n",
            "Epoch 385/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 4.7363 - mse: 31.9289\n",
            "Epoch 386/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.7066 - mse: 32.0434\n",
            "Epoch 387/500\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 4.6938 - mse: 32.3360\n",
            "Epoch 388/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 4.6969 - mse: 32.7033\n",
            "Epoch 389/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 4.6996 - mse: 32.9107\n",
            "Epoch 390/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 4.7003 - mse: 33.0701\n",
            "Epoch 391/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 4.7246 - mse: 33.4280\n",
            "Epoch 392/500\n",
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            "Epoch 393/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 4.6918 - mse: 33.0202\n",
            "Epoch 394/500\n",
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            "Epoch 395/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 4.6049 - mse: 31.3654\n",
            "Epoch 396/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 4.5897 - mse: 30.6437\n",
            "Epoch 397/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 4.6125 - mse: 30.1503\n",
            "Epoch 398/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 4.6360 - mse: 29.7251\n",
            "Epoch 399/500\n",
            "2/2 [==============================] - 0s 29ms/step - loss: 4.6253 - mse: 29.5625\n",
            "Epoch 400/500\n",
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            "Epoch 401/500\n",
            "2/2 [==============================] - 0s 35ms/step - loss: 4.5278 - mse: 29.8577\n",
            "Epoch 402/500\n",
            "2/2 [==============================] - 0s 29ms/step - loss: 4.5461 - mse: 30.5470\n",
            "Epoch 403/500\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 4.5513 - mse: 30.9050\n",
            "Epoch 404/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 4.5670 - mse: 31.1301\n",
            "Epoch 405/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 4.5457 - mse: 30.9025\n",
            "Epoch 406/500\n",
            "2/2 [==============================] - 0s 32ms/step - loss: 4.5173 - mse: 30.2595\n",
            "Epoch 407/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 4.4792 - mse: 29.6715\n",
            "Epoch 408/500\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 4.4786 - mse: 29.2592\n",
            "Epoch 409/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 4.4629 - mse: 28.7987\n",
            "Epoch 410/500\n",
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            "Epoch 412/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 4.4335 - mse: 29.1305\n",
            "Epoch 413/500\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 4.4391 - mse: 29.3964\n",
            "Epoch 414/500\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 4.4417 - mse: 29.4617\n",
            "Epoch 415/500\n",
            "2/2 [==============================] - 0s 27ms/step - loss: 4.4330 - mse: 29.3451\n",
            "Epoch 416/500\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 4.3941 - mse: 28.7743\n",
            "Epoch 417/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 4.3727 - mse: 28.0767\n",
            "Epoch 418/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 4.3728 - mse: 27.3592\n",
            "Epoch 419/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 4.3782 - mse: 26.9939\n",
            "Epoch 420/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.3722 - mse: 26.8371\n",
            "Epoch 421/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 4.3546 - mse: 26.7624\n",
            "Epoch 422/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 4.3371 - mse: 26.6458\n",
            "Epoch 423/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 4.3214 - mse: 26.5368\n",
            "Epoch 424/500\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 4.3054 - mse: 26.4763\n",
            "Epoch 425/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.2827 - mse: 26.4648\n",
            "Epoch 426/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.2744 - mse: 26.5884\n",
            "Epoch 427/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 4.2560 - mse: 26.6032\n",
            "Epoch 428/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 4.2536 - mse: 26.6204\n",
            "Epoch 429/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.2489 - mse: 26.5527\n",
            "Epoch 430/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 4.2290 - mse: 26.3779\n",
            "Epoch 431/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.2137 - mse: 26.2773\n",
            "Epoch 432/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 4.1969 - mse: 25.8333\n",
            "Epoch 433/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.1918 - mse: 25.3966\n",
            "Epoch 434/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.1963 - mse: 25.0104\n",
            "Epoch 435/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.1866 - mse: 24.8352\n",
            "Epoch 436/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 4.1614 - mse: 24.8035\n",
            "Epoch 437/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 4.1363 - mse: 24.9708\n",
            "Epoch 438/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 4.1398 - mse: 25.3112\n",
            "Epoch 439/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 4.1426 - mse: 25.5739\n",
            "Epoch 440/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 4.1417 - mse: 25.5961\n",
            "Epoch 441/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 4.1296 - mse: 25.4485\n",
            "Epoch 442/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 4.1111 - mse: 25.1840\n",
            "Epoch 443/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 4.0859 - mse: 24.7954\n",
            "Epoch 444/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 4.0663 - mse: 24.2778\n",
            "Epoch 445/500\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 4.0617 - mse: 23.8637\n",
            "Epoch 446/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 4.0461 - mse: 23.5863\n",
            "Epoch 447/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 4.0331 - mse: 23.4771\n",
            "Epoch 448/500\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 4.0196 - mse: 23.3959\n",
            "Epoch 449/500\n",
            "2/2 [==============================] - 0s 45ms/step - loss: 4.0041 - mse: 23.3265\n",
            "Epoch 450/500\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 3.9906 - mse: 23.2863\n",
            "Epoch 451/500\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 3.9802 - mse: 23.2577\n",
            "Epoch 452/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 3.9664 - mse: 23.3272\n",
            "Epoch 453/500\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 3.9937 - mse: 23.7148\n",
            "Epoch 454/500\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 3.9903 - mse: 23.7976\n",
            "Epoch 455/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 3.9710 - mse: 23.5807\n",
            "Epoch 456/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 3.9482 - mse: 23.1864\n",
            "Epoch 457/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 3.9052 - mse: 22.5806\n",
            "Epoch 458/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 3.8969 - mse: 22.1189\n",
            "Epoch 459/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 3.9029 - mse: 21.5629\n",
            "Epoch 460/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 3.9296 - mse: 21.2775\n",
            "Epoch 461/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 3.9131 - mse: 21.1240\n",
            "Epoch 462/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 3.8830 - mse: 21.3253\n",
            "Epoch 463/500\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 3.8382 - mse: 21.3992\n",
            "Epoch 464/500\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 3.8233 - mse: 21.3551\n",
            "Epoch 465/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 3.8101 - mse: 21.1610\n",
            "Epoch 466/500\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 3.7989 - mse: 20.9769\n",
            "Epoch 467/500\n",
            "2/2 [==============================] - 0s 35ms/step - loss: 3.7847 - mse: 20.8503\n",
            "Epoch 468/500\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 3.7749 - mse: 20.8643\n",
            "Epoch 469/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 3.7615 - mse: 20.8533\n",
            "Epoch 470/500\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 3.7509 - mse: 20.7801\n",
            "Epoch 471/500\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 3.7354 - mse: 20.5602\n",
            "Epoch 472/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 3.7195 - mse: 20.2048\n",
            "Epoch 473/500\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 3.7145 - mse: 19.8831\n",
            "Epoch 474/500\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 3.7062 - mse: 19.6585\n",
            "Epoch 475/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 3.6893 - mse: 19.5810\n",
            "Epoch 476/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 3.6682 - mse: 19.5774\n",
            "Epoch 477/500\n",
            "2/2 [==============================] - 0s 30ms/step - loss: 3.6558 - mse: 19.7128\n",
            "Epoch 478/500\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 3.6717 - mse: 20.0823\n",
            "Epoch 479/500\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 3.6712 - mse: 20.1507\n",
            "Epoch 480/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 3.6486 - mse: 19.9039\n",
            "Epoch 481/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 3.6201 - mse: 19.4100\n",
            "Epoch 482/500\n",
            "2/2 [==============================] - 0s 27ms/step - loss: 3.5898 - mse: 18.8892\n",
            "Epoch 483/500\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 3.5920 - mse: 18.5376\n",
            "Epoch 484/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 3.5848 - mse: 18.2279\n",
            "Epoch 485/500\n",
            "2/2 [==============================] - 0s 31ms/step - loss: 3.5725 - mse: 18.0925\n",
            "Epoch 486/500\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 3.5540 - mse: 18.0366\n",
            "Epoch 487/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 3.5289 - mse: 18.0364\n",
            "Epoch 488/500\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 3.5147 - mse: 18.0928\n",
            "Epoch 489/500\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 3.5048 - mse: 18.1245\n",
            "Epoch 490/500\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 3.4901 - mse: 18.0141\n",
            "Epoch 491/500\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 3.4814 - mse: 17.6959\n",
            "Epoch 492/500\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 3.4596 - mse: 17.3808\n",
            "Epoch 493/500\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 3.4472 - mse: 17.2253\n",
            "Epoch 494/500\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 3.4323 - mse: 17.1036\n",
            "Epoch 495/500\n",
            "2/2 [==============================] - 0s 29ms/step - loss: 3.4155 - mse: 17.0309\n",
            "Epoch 496/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 3.4050 - mse: 17.0143\n",
            "Epoch 497/500\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 3.3924 - mse: 16.9408\n",
            "Epoch 498/500\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 3.3772 - mse: 16.8620\n",
            "Epoch 499/500\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 3.3702 - mse: 16.8629\n",
            "Epoch 500/500\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 3.3805 - mse: 16.9755\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e42f5dc6a10>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds_3 = model_3.predict(X_test)\n",
        "plot_predictions(predictions = y_preds_3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "Nn1k7UDLkE3v",
        "outputId": "6cd23a33-33ac-4ecc-b8c2-58c7cd5ee72e"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 164ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate model_3 evaluation metrics\n",
        "mae_3 = mae(y_test, y_preds_3)\n",
        "mse_3 = mse(y_test, y_preds_3)\n",
        "\n",
        "mae_3, mse_3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHmmL4kokdH9",
        "outputId": "0d94650f-1f7a-47e1-d9cd-05ed8197e068"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8.968459, 80.890205)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Comparing the results of our experiments\n"
      ],
      "metadata": {
        "id": "QgHirHW5lGBd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparing our model's results using pandas dataframe\n",
        "import pandas as pd\n",
        "\n",
        "model_results = [[\"model_1\", mae_1, mse_1],\n",
        "                 [\"model_2\", mae_2, mse_2],\n",
        "                 [\"model_3\", mae_3, mse_3]]\n",
        "\n",
        "all_results = pd.DataFrame(model_results, columns = [\"Model\", \"MAE\", \"MSE\"])\n",
        "all_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "doM3zfbTmsqu",
        "outputId": "418a36e5-311a-42f4-f4d3-a1cc27021475"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Model        MAE          MSE\n",
              "0  model_1   8.325836    75.623619\n",
              "1  model_2  48.581886  2391.438965\n",
              "2  model_3   8.968459    80.890205"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b474d28-a944-4594-9ac1-f44955475cd2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>model_1</td>\n",
              "      <td>8.325836</td>\n",
              "      <td>75.623619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>model_2</td>\n",
              "      <td>48.581886</td>\n",
              "      <td>2391.438965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>model_3</td>\n",
              "      <td>8.968459</td>\n",
              "      <td>80.890205</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b474d28-a944-4594-9ac1-f44955475cd2')\"\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-6b474d28-a944-4594-9ac1-f44955475cd2 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-6b474d28-a944-4594-9ac1-f44955475cd2');\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-9f5b011e-33f4-47a8-9c3e-4f73b23af6a1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9f5b011e-33f4-47a8-9c3e-4f73b23af6a1')\"\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-9f5b011e-33f4-47a8-9c3e-4f73b23af6a1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 3 performed the best"
      ],
      "metadata": {
        "id": "yBAyMaOWn8JG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mfKJfQ55ncXU",
        "outputId": "d0bf9202-9817-4eb5-eace-cf6c9d2c0d7d"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_8 (Dense)             (None, 10)                20        \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 31 (124.00 Byte)\n",
            "Trainable params: 31 (124.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tracking Experiments\n",
        "\n",
        "* Tensorboard - a component of the TensorFLow lib to help track modelling experiments.\n",
        "* Weights & biases - a tool for tracking all kinds of machine learning experiments (plus straight into TensorBoard)"
      ],
      "metadata": {
        "id": "HYN5CjF2nyLs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving a model"
      ],
      "metadata": {
        "id": "83I-4pSTofU4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.save('best_one_SavedModel_format')"
      ],
      "metadata": {
        "id": "gVeofpBToo_n"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model using the HDF5 format\n",
        "model_3.save(\"my_model.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VvdE9MJUpFIz",
        "outputId": "6eb7ea47-9824-4830-c9be-7d220c39e12d"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading in a saved model"
      ],
      "metadata": {
        "id": "26btOd2TIJtc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load in the SavedModel format model\n",
        "loaded_SavedModel_format = tf.keras.models.load_model('/content/best_one_SavedModel_format')\n",
        "loaded_SavedModel_format.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tin0eYBXIgv4",
        "outputId": "c43a7548-ca34-437d-bae0-78d4f4348b47"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_8 (Dense)             (None, 10)                20        \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 31 (124.00 Byte)\n",
            "Trainable params: 31 (124.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uTnLQVTGI1IP",
        "outputId": "3376ce24-b234-4d92-a650-36d44e01cb9a"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_8 (Dense)             (None, 10)                20        \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 31 (124.00 Byte)\n",
            "Trainable params: 31 (124.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Compare model_3 predictions with SavedModel format model predictions\n",
        "model_3_preds = model_3.predict(X_test)\n",
        "loaded_SavedModel_format_preds = loaded_SavedModel_format.predict(X_test)\n",
        "model_3_preds == loaded_SavedModel_format_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rl63xo8qI7LY",
        "outputId": "91a187b8-737a-451e-962c-c46fa59c1de7"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True]])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mae(y_test, model_3_preds) == mae(y_test, loaded_SavedModel_format_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "loDBWH37JVbt",
        "outputId": "b9a11112-3a23-4104-dcae-f6ea97644250"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load in a model using the h5 format\n",
        "loaded_h5_model = tf.keras.models.load_model('/content/my_model.h5')\n",
        "loaded_h5_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iDO7_cbnJkqn",
        "outputId": "72fa1661-5db6-491d-ebbf-b52d2ea688f3"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_8 (Dense)             (None, 10)                20        \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 31 (124.00 Byte)\n",
            "Trainable params: 31 (124.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check to see if loaded .h5 model predictions match model_3 preds\n",
        "model_3_preds = model_3.predict(X_test)\n",
        "loaded_h5_preds = loaded_h5_model.predict(X_test)\n",
        "\n",
        "model_3_preds == loaded_h5_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frIhvCzUKDUV",
        "outputId": "0cbaec1f-b67b-4da3-b2f1-c6fe07fab700"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 84ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True]])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mae(y_test, model_3_preds) == mae(y_test, loaded_h5_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dVLEzijTKZYF",
        "outputId": "2b8c6ae9-ffb2-4c47-da43-b3b0d06ce19d"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download a model(or any other file) from Google Colab"
      ],
      "metadata": {
        "id": "P6EgDl-JKsso"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download a file from Google Colab\n",
        "from google.colab import files\n",
        "files.download('/content/my_model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "GIW8e5KuK9HR",
        "outputId": "bf29fc5a-bedc-4349-dbc9-cf7fbe10023e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_7ce5b978-9858-40e7-8bba-efe6825de5f6\", \"my_model.h5\", 26200)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## A Larger Example"
      ],
      "metadata": {
        "id": "mwZflbUjLICU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import required libraries\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "BA7L8SgNLS_z"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read in the insurance dataset\n",
        "insurance = pd.read_csv('https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv')\n",
        "insurance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "hyHqMrWTMlIf",
        "outputId": "848093d7-0808-40af-8fb9-053697308f5c"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age     sex     bmi  children smoker     region      charges\n",
              "0      19  female  27.900         0    yes  southwest  16884.92400\n",
              "1      18    male  33.770         1     no  southeast   1725.55230\n",
              "2      28    male  33.000         3     no  southeast   4449.46200\n",
              "3      33    male  22.705         0     no  northwest  21984.47061\n",
              "4      32    male  28.880         0     no  northwest   3866.85520\n",
              "...   ...     ...     ...       ...    ...        ...          ...\n",
              "1333   50    male  30.970         3     no  northwest  10600.54830\n",
              "1334   18  female  31.920         0     no  northeast   2205.98080\n",
              "1335   18  female  36.850         0     no  southeast   1629.83350\n",
              "1336   21  female  25.800         0     no  southwest   2007.94500\n",
              "1337   61  female  29.070         0    yes  northwest  29141.36030\n",
              "\n",
              "[1338 rows x 7 columns]"
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              "      <td>1629.83350</td>\n",
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              "      <th>1336</th>\n",
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              "      <td>25.800</td>\n",
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              "<p>1338 rows × 7 columns</p>\n",
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              "        document.querySelector('#df-f2715e0c-6044-47ac-870d-16c83c664d81 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-f2715e0c-6044-47ac-870d-16c83c664d81');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# One hot encoding the DataFrame so its all numbers\n",
        "insurance_one_hot = pd.get_dummies(insurance)\n",
        "insurance_one_hot.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "i6wm5tvvMtf1",
        "outputId": "68260550-b9dd-404d-ea08-a4def4095ddc"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "   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",
              "   region_southwest  \n",
              "0                 1  \n",
              "1                 0  \n",
              "2                 0  \n",
              "3                 0  \n",
              "4                 0  "
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>21984.47061</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>3866.85520</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0044c41-efad-45ac-97b0-0b60c9798f76')\"\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-c0044c41-efad-45ac-97b0-0b60c9798f76 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-c0044c41-efad-45ac-97b0-0b60c9798f76');\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-a240ae70-aaef-4bd7-8101-573e7363a487\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a240ae70-aaef-4bd7-8101-573e7363a487')\"\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-a240ae70-aaef-4bd7-8101-573e7363a487 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create X and y values\n",
        "X = insurance_one_hot.drop(\"charges\", axis = 1)\n",
        "y = insurance_one_hot[\"charges\"]"
      ],
      "metadata": {
        "id": "p4C2R5PZNswH"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# View X and y\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "gmwJOGNqOc23",
        "outputId": "0091d097-99f7-41b1-8182-7f94c52dffa3"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age     bmi  children  sex_female  sex_male  smoker_no  smoker_yes  \\\n",
              "0   19  27.900         0           1         0          0           1   \n",
              "1   18  33.770         1           0         1          1           0   \n",
              "2   28  33.000         3           0         1          1           0   \n",
              "3   33  22.705         0           0         1          1           0   \n",
              "4   32  28.880         0           0         1          1           0   \n",
              "\n",
              "   region_northeast  region_northwest  region_southeast  region_southwest  \n",
              "0                 0                 0                 0                 1  \n",
              "1                 0                 0                 1                 0  \n",
              "2                 0                 0                 1                 0  \n",
              "3                 0                 1                 0                 0  \n",
              "4                 0                 1                 0                 0  "
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>sex_female</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>smoker_no</th>\n",
              "      <th>smoker_yes</th>\n",
              "      <th>region_northeast</th>\n",
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              "      <th>region_southeast</th>\n",
              "      <th>region_southwest</th>\n",
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              "      <td>33.000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: 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",
              "    }\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-6b1385fd-f4e8-4811-8011-cdb53e5baa8b 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-6b1385fd-f4e8-4811-8011-cdb53e5baa8b');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-12bac617-f867-4abe-a125-8b896177bdba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvmSdvgCOfst",
        "outputId": "c4a615cb-b77d-4b8c-ad33-156d1ce863ad"
      },
      "execution_count": 74,
      "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",
              "Name: charges, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create training and test split\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
        "len(X), len(X_train), len(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RRPBqHkrOjZ1",
        "outputId": "2e1f1b29-1a16-4a80-a79b-32df6553ebae"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1338, 1070, 268)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a neural network\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create a model\n",
        "insurance_model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "insurance_model.compile(loss = tf.keras.losses.mae,\n",
        "                  optimizer = tf.keras.optimizers.SGD(),\n",
        "                  metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "insurance_model.fit(X_train, y_train, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqzFMc4-O0-0",
        "outputId": "0db0a9b5-f7a7-424f-aa06-0ba96cf30211"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 2s 4ms/step - loss: 8585.5898 - mae: 8585.5898\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 7878.2329 - mae: 7878.2329\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 7592.4971 - mae: 7592.4971\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7555.9609 - mae: 7555.9609\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7611.7544 - mae: 7611.7544\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7578.0479 - mae: 7578.0479\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7513.8677 - mae: 7513.8677\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 7799.4614 - mae: 7799.4614\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7605.6621 - mae: 7605.6621\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7656.4541 - mae: 7656.4541\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7622.6660 - mae: 7622.6660\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7634.6226 - mae: 7634.6226\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7678.9707 - mae: 7678.9707\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7808.0381 - mae: 7808.0381\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7378.4497 - mae: 7378.4497\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 7667.7095 - mae: 7667.7095\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 7523.7441 - mae: 7523.7441\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7813.9980 - mae: 7813.9980\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 7494.2363 - mae: 7494.2363\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7880.5122 - mae: 7880.5122\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7491.8999 - mae: 7491.8999\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7897.5942 - mae: 7897.5942\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7648.0815 - mae: 7648.0815\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7557.8745 - mae: 7557.8745\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7686.8301 - mae: 7686.8301\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7675.6592 - mae: 7675.6592\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7538.9990 - mae: 7538.9990\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7331.3564 - mae: 7331.3564\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7687.8647 - mae: 7687.8647\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7439.3784 - mae: 7439.3784\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7608.4478 - mae: 7608.4478\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7472.5137 - mae: 7472.5137\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7375.7891 - mae: 7375.7891\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7566.7676 - mae: 7566.7676\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7590.7798 - mae: 7590.7798\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7564.6807 - mae: 7564.6807\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 7631.6177 - mae: 7631.6177\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7403.8120 - mae: 7403.8120\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7363.3638 - mae: 7363.3638\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7301.2153 - mae: 7301.2153\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7677.9604 - mae: 7677.9604\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7359.7183 - mae: 7359.7183\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7695.9131 - mae: 7695.9131\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7507.2949 - mae: 7507.2949\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7274.8662 - mae: 7274.8662\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7463.8979 - mae: 7463.8979\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7431.3179 - mae: 7431.3179\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7622.3765 - mae: 7622.3765\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7541.1265 - mae: 7541.1265\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7476.6860 - mae: 7476.6860\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7387.9829 - mae: 7387.9829\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7516.3066 - mae: 7516.3066\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7550.8364 - mae: 7550.8364\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7294.3745 - mae: 7294.3745\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7190.5737 - mae: 7190.5737\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7247.3472 - mae: 7247.3472\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7483.5273 - mae: 7483.5273\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7517.2871 - mae: 7517.2871\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7637.4468 - mae: 7637.4468\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7457.8320 - mae: 7457.8320\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7313.0317 - mae: 7313.0317\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7505.3984 - mae: 7505.3984\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7292.0742 - mae: 7292.0742\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7277.9624 - mae: 7277.9624\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7282.6372 - mae: 7282.6372\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7286.9902 - mae: 7286.9902\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7520.1035 - mae: 7520.1035\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7598.5542 - mae: 7598.5542\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7557.7661 - mae: 7557.7661\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7709.2759 - mae: 7709.2759\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7385.1992 - mae: 7385.1992\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7441.6675 - mae: 7441.6675\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7464.6313 - mae: 7464.6313\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7318.9834 - mae: 7318.9834\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7329.6064 - mae: 7329.6064\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7119.9297 - mae: 7119.9297\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7362.9585 - mae: 7362.9585\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7243.6680 - mae: 7243.6680\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7560.2930 - mae: 7560.2930\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7356.5649 - mae: 7356.5649\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7272.6646 - mae: 7272.6646\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7202.1846 - mae: 7202.1846\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7300.7148 - mae: 7300.7148\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7353.6914 - mae: 7353.6914\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7286.0654 - mae: 7286.0654\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7553.9497 - mae: 7553.9497\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7295.6538 - mae: 7295.6538\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7322.4395 - mae: 7322.4395\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7202.8145 - mae: 7202.8145\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 6943.7017 - mae: 6943.7017\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7199.4321 - mae: 7199.4321\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7290.7734 - mae: 7290.7734\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7495.1577 - mae: 7495.1577\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7326.0713 - mae: 7326.0713\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7410.3135 - mae: 7410.3135\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7182.2505 - mae: 7182.2505\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7306.4648 - mae: 7306.4648\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7320.3213 - mae: 7320.3213\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7249.5518 - mae: 7249.5518\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7250.4214 - mae: 7250.4214\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e42e5462890>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the results of the insurance model on the test data\n",
        "insurance_model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuyRgeZOPhQI",
        "outputId": "b8394944-01d5-4471-9008-60b42cf7a326"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 8ms/step - loss: 7658.2559 - mae: 7658.2559\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7658.255859375, 7658.255859375]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.median(), y_train.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayUfSUuAP6d3",
        "outputId": "e27c64f6-4eba-4a57-e5ad-ad2ea99614c5"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9575.4421, 13346.089736364485)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To try to improve the model, trying 2 experiments:\n",
        "1. Add an extra layer with more hidden units\n",
        "2. Train for longer"
      ],
      "metadata": {
        "id": "rcXZmqw0QBuT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model\n",
        "insurance_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",
        "# 2. Compile the model\n",
        "insurance_model_2.compile(loss = tf.keras.losses.mae,\n",
        "                          optimizer = tf.keras.optimizers.Adam(),\n",
        "                          metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "insurance_model_2.fit(X_train, y_train, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nIQFWaaGQYe2",
        "outputId": "bb1f6fff-bdd9-429a-bdae-8522466b0262"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 2s 4ms/step - loss: 13294.8867 - mae: 13294.8867\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13139.0010 - mae: 13139.0010\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12810.6377 - mae: 12810.6377\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12157.4717 - mae: 12157.4717\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 11043.8057 - mae: 11043.8057\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 9593.1260 - mae: 9593.1260\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 8243.6221 - mae: 8243.6221\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 7562.6841 - mae: 7562.6841\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 7444.1284 - mae: 7444.1284\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 7422.3887 - mae: 7422.3887\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 7403.2021 - mae: 7403.2021\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 7380.5762 - mae: 7380.5762\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 7359.6914 - mae: 7359.6914\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7336.9434 - mae: 7336.9434\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 7317.2910 - mae: 7317.2910\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7294.4399 - mae: 7294.4399\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7273.4858 - mae: 7273.4858\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7249.8394 - mae: 7249.8394\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 9ms/step - loss: 7226.8511 - mae: 7226.8511\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 7202.8315 - mae: 7202.8315\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 7183.1606 - mae: 7183.1606\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7155.4575 - mae: 7155.4575\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 7129.6812 - mae: 7129.6812\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7104.2173 - mae: 7104.2173\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7085.5166 - mae: 7085.5166\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7052.9531 - mae: 7052.9531\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 7023.3608 - mae: 7023.3608\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 6994.3555 - mae: 6994.3555\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 6965.4424 - mae: 6965.4424\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6936.7964 - mae: 6936.7964\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6904.6792 - mae: 6904.6792\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6875.0645 - mae: 6875.0645\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6842.2627 - mae: 6842.2627\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6809.9507 - mae: 6809.9507\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6773.9463 - mae: 6773.9463\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6737.8154 - mae: 6737.8154\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6700.4028 - mae: 6700.4028\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6667.5679 - mae: 6667.5679\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6628.5459 - mae: 6628.5459\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6591.8906 - mae: 6591.8906\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6561.2866 - mae: 6561.2866\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6534.0747 - mae: 6534.0747\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6506.9946 - mae: 6506.9946\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6485.3306 - mae: 6485.3306\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6476.0781 - mae: 6476.0781\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 6455.8076 - mae: 6455.8076\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6439.8623 - mae: 6439.8623\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 6425.4907 - mae: 6425.4907\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6413.0200 - mae: 6413.0200\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6398.5020 - mae: 6398.5020\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6384.6846 - mae: 6384.6846\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6372.6665 - mae: 6372.6665\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6357.3960 - mae: 6357.3960\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6342.7661 - mae: 6342.7661\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6329.0752 - mae: 6329.0752\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6314.9849 - mae: 6314.9849\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6301.1455 - mae: 6301.1455\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6287.0361 - mae: 6287.0361\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6270.2461 - mae: 6270.2461\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6259.1318 - mae: 6259.1318\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6238.6030 - mae: 6238.6030\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6225.6602 - mae: 6225.6602\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6206.5161 - mae: 6206.5161\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6188.1978 - mae: 6188.1978\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6170.0093 - mae: 6170.0093\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6151.5757 - mae: 6151.5757\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6139.2754 - mae: 6139.2754\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6114.9897 - mae: 6114.9897\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6097.0215 - mae: 6097.0215\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6075.6343 - mae: 6075.6343\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6055.3774 - mae: 6055.3774\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6032.9595 - mae: 6032.9595\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 6019.1372 - mae: 6019.1372\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5988.4170 - mae: 5988.4170\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5966.3081 - mae: 5966.3081\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5948.7632 - mae: 5948.7632\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5915.1577 - mae: 5915.1577\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5890.2080 - mae: 5890.2080\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5864.0171 - mae: 5864.0171\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5833.9663 - mae: 5833.9663\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5806.7959 - mae: 5806.7959\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5775.1245 - mae: 5775.1245\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5745.2744 - mae: 5745.2744\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5710.0190 - mae: 5710.0190\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5681.6914 - mae: 5681.6914\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5644.5845 - mae: 5644.5845\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5604.8755 - mae: 5604.8755\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5566.8574 - mae: 5566.8574\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5524.3501 - mae: 5524.3501\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5479.3271 - mae: 5479.3271\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5437.5269 - mae: 5437.5269\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5387.5503 - mae: 5387.5503\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5341.5752 - mae: 5341.5752\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5291.0728 - mae: 5291.0728\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5236.3081 - mae: 5236.3081\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5187.2231 - mae: 5187.2231\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5130.7231 - mae: 5130.7231\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5063.6382 - mae: 5063.6382\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4999.4028 - mae: 4999.4028\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4941.2612 - mae: 4941.2612\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e42eee3dab0>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "insurance_model_2.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7kVQI1zQuNF",
        "outputId": "2f915861-6a50-4600-e107-3c03fee23b2a"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 4811.7437 - mae: 4811.7437\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4811.74365234375, 4811.74365234375]"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create the model\n",
        "insurance_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",
        "# 2. Compile the model\n",
        "insurance_model_3.compile(loss = tf.keras.losses.mae,\n",
        "                          optimizer = tf.keras.optimizers.Adam(),\n",
        "                          metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = insurance_model_3.fit(X_train, y_train, epochs = 200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kz3xhe_DRJnT",
        "outputId": "2981eec2-3e53-4943-d233-629133cda8dc"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "34/34 [==============================] - 3s 7ms/step - loss: 13290.8232 - mae: 13290.8232\n",
            "Epoch 2/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 13126.5840 - mae: 13126.5840\n",
            "Epoch 3/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 12783.4004 - mae: 12783.4004\n",
            "Epoch 4/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 12115.6953 - mae: 12115.6953\n",
            "Epoch 5/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 11004.9902 - mae: 11004.9902\n",
            "Epoch 6/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 9584.8896 - mae: 9584.8896\n",
            "Epoch 7/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 8260.5645 - mae: 8260.5645\n",
            "Epoch 8/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7577.1494 - mae: 7577.1494\n",
            "Epoch 9/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7452.0596 - mae: 7452.0596\n",
            "Epoch 10/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7431.1006 - mae: 7431.1006\n",
            "Epoch 11/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7412.5063 - mae: 7412.5063\n",
            "Epoch 12/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7391.1245 - mae: 7391.1245\n",
            "Epoch 13/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7371.0737 - mae: 7371.0737\n",
            "Epoch 14/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7349.4443 - mae: 7349.4443\n",
            "Epoch 15/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7330.7729 - mae: 7330.7729\n",
            "Epoch 16/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7308.6323 - mae: 7308.6323\n",
            "Epoch 17/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7288.9824 - mae: 7288.9824\n",
            "Epoch 18/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7265.9834 - mae: 7265.9834\n",
            "Epoch 19/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7244.0317 - mae: 7244.0317\n",
            "Epoch 20/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7221.4712 - mae: 7221.4712\n",
            "Epoch 21/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7201.0073 - mae: 7201.0073\n",
            "Epoch 22/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7176.4512 - mae: 7176.4512\n",
            "Epoch 23/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7152.1538 - mae: 7152.1538\n",
            "Epoch 24/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7127.6992 - mae: 7127.6992\n",
            "Epoch 25/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7110.4292 - mae: 7110.4292\n",
            "Epoch 26/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7078.8442 - mae: 7078.8442\n",
            "Epoch 27/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7050.6421 - mae: 7050.6421\n",
            "Epoch 28/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7023.0405 - mae: 7023.0405\n",
            "Epoch 29/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6995.7393 - mae: 6995.7393\n",
            "Epoch 30/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6968.6484 - mae: 6968.6484\n",
            "Epoch 31/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6937.8965 - mae: 6937.8965\n",
            "Epoch 32/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6910.4053 - mae: 6910.4053\n",
            "Epoch 33/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6879.1377 - mae: 6879.1377\n",
            "Epoch 34/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6846.9907 - mae: 6846.9907\n",
            "Epoch 35/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6814.4897 - mae: 6814.4897\n",
            "Epoch 36/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6780.5498 - mae: 6780.5498\n",
            "Epoch 37/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6744.9644 - mae: 6744.9644\n",
            "Epoch 38/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6711.9404 - mae: 6711.9404\n",
            "Epoch 39/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6673.0908 - mae: 6673.0908\n",
            "Epoch 40/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6636.9805 - mae: 6636.9805\n",
            "Epoch 41/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6602.3442 - mae: 6602.3442\n",
            "Epoch 42/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6571.5957 - mae: 6571.5957\n",
            "Epoch 43/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6539.4629 - mae: 6539.4629\n",
            "Epoch 44/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6512.6357 - mae: 6512.6357\n",
            "Epoch 45/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 6493.1265 - mae: 6493.1265\n",
            "Epoch 46/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6475.1016 - mae: 6475.1016\n",
            "Epoch 47/200\n",
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            "Epoch 48/200\n",
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            "Epoch 49/200\n",
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            "Epoch 50/200\n",
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            "Epoch 51/200\n",
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            "Epoch 52/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6388.7393 - mae: 6388.7393\n",
            "Epoch 53/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6374.5366 - mae: 6374.5366\n",
            "Epoch 54/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6360.7500 - mae: 6360.7500\n",
            "Epoch 55/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6347.2402 - mae: 6347.2402\n",
            "Epoch 56/200\n",
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            "Epoch 57/200\n",
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            "Epoch 58/200\n",
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            "Epoch 59/200\n",
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            "Epoch 60/200\n",
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            "Epoch 61/200\n",
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            "Epoch 62/200\n",
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            "Epoch 63/200\n",
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            "Epoch 64/200\n",
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            "Epoch 65/200\n",
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            "Epoch 66/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6180.7793 - mae: 6180.7793\n",
            "Epoch 67/200\n",
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            "Epoch 68/200\n",
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            "Epoch 69/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6130.3335 - mae: 6130.3335\n",
            "Epoch 70/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6110.2300 - mae: 6110.2300\n",
            "Epoch 71/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6090.8994 - mae: 6090.8994\n",
            "Epoch 72/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6073.2446 - mae: 6073.2446\n",
            "Epoch 73/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6057.8013 - mae: 6057.8013\n",
            "Epoch 74/200\n",
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            "Epoch 75/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6009.5396 - mae: 6009.5396\n",
            "Epoch 76/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5996.9912 - mae: 5996.9912\n",
            "Epoch 77/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5964.0200 - mae: 5964.0200\n",
            "Epoch 78/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5941.4229 - mae: 5941.4229\n",
            "Epoch 79/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5917.8013 - mae: 5917.8013\n",
            "Epoch 80/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5891.1816 - mae: 5891.1816\n",
            "Epoch 81/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5866.0381 - mae: 5866.0381\n",
            "Epoch 82/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5838.6323 - mae: 5838.6323\n",
            "Epoch 83/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5810.9448 - mae: 5810.9448\n",
            "Epoch 84/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5778.9219 - mae: 5778.9219\n",
            "Epoch 85/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5753.1997 - mae: 5753.1997\n",
            "Epoch 86/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5719.4351 - mae: 5719.4351\n",
            "Epoch 87/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5684.0312 - mae: 5684.0312\n",
            "Epoch 88/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5650.7944 - mae: 5650.7944\n",
            "Epoch 89/200\n",
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            "Epoch 90/200\n",
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            "Epoch 91/200\n",
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            "Epoch 92/200\n",
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            "Epoch 93/200\n",
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            "Epoch 94/200\n",
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            "Epoch 95/200\n",
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            "Epoch 96/200\n",
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            "Epoch 97/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5261.7988 - mae: 5261.7988\n",
            "Epoch 98/200\n",
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            "Epoch 99/200\n",
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            "Epoch 100/200\n",
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            "Epoch 101/200\n",
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            "Epoch 102/200\n",
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            "Epoch 103/200\n",
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            "Epoch 104/200\n",
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            "Epoch 105/200\n",
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            "Epoch 106/200\n",
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            "Epoch 107/200\n",
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            "Epoch 108/200\n",
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            "Epoch 109/200\n",
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            "Epoch 110/200\n",
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            "Epoch 111/200\n",
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            "Epoch 112/200\n",
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            "Epoch 113/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4090.2908 - mae: 4090.2908\n",
            "Epoch 114/200\n",
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            "Epoch 115/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3973.2598 - mae: 3973.2598\n",
            "Epoch 116/200\n",
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            "Epoch 117/200\n",
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            "Epoch 118/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3895.0315 - mae: 3895.0315\n",
            "Epoch 119/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3867.4807 - mae: 3867.4807\n",
            "Epoch 120/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3846.9302 - mae: 3846.9302\n",
            "Epoch 121/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3840.7007 - mae: 3840.7007\n",
            "Epoch 122/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3832.9905 - mae: 3832.9905\n",
            "Epoch 123/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3825.8848 - mae: 3825.8848\n",
            "Epoch 124/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3816.1394 - mae: 3816.1394\n",
            "Epoch 125/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3803.8494 - mae: 3803.8494\n",
            "Epoch 126/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3816.0898 - mae: 3816.0898\n",
            "Epoch 127/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3803.8748 - mae: 3803.8748\n",
            "Epoch 128/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3798.3445 - mae: 3798.3445\n",
            "Epoch 129/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3805.2576 - mae: 3805.2576\n",
            "Epoch 130/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3793.1411 - mae: 3793.1411\n",
            "Epoch 131/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3786.6453 - mae: 3786.6453\n",
            "Epoch 132/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3780.1050 - mae: 3780.1050\n",
            "Epoch 133/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3776.2410 - mae: 3776.2410\n",
            "Epoch 134/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3775.1426 - mae: 3775.1426\n",
            "Epoch 135/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3771.4653 - mae: 3771.4653\n",
            "Epoch 136/200\n",
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            "Epoch 137/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3777.8281 - mae: 3777.8281\n",
            "Epoch 138/200\n",
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            "Epoch 139/200\n",
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            "Epoch 140/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3768.5383 - mae: 3768.5383\n",
            "Epoch 141/200\n",
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            "Epoch 142/200\n",
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            "Epoch 143/200\n",
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            "Epoch 144/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3754.8599 - mae: 3754.8599\n",
            "Epoch 145/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3755.5271 - mae: 3755.5271\n",
            "Epoch 146/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3760.1199 - mae: 3760.1199\n",
            "Epoch 147/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3756.0613 - mae: 3756.0613\n",
            "Epoch 148/200\n",
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            "Epoch 149/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3747.8384 - mae: 3747.8384\n",
            "Epoch 150/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3742.4824 - mae: 3742.4824\n",
            "Epoch 151/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3746.4199 - mae: 3746.4199\n",
            "Epoch 152/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3748.0955 - mae: 3748.0955\n",
            "Epoch 153/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3746.8235 - mae: 3746.8235\n",
            "Epoch 154/200\n",
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            "Epoch 155/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3743.7463 - mae: 3743.7463\n",
            "Epoch 156/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3738.8740 - mae: 3738.8740\n",
            "Epoch 157/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3737.4707 - mae: 3737.4707\n",
            "Epoch 158/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3733.3469 - mae: 3733.3469\n",
            "Epoch 159/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3729.3638 - mae: 3729.3638\n",
            "Epoch 160/200\n",
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            "Epoch 161/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3731.9963 - mae: 3731.9963\n",
            "Epoch 162/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3727.9634 - mae: 3727.9634\n",
            "Epoch 163/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3726.7756 - mae: 3726.7756\n",
            "Epoch 164/200\n",
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            "Epoch 165/200\n",
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            "Epoch 166/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3724.8391 - mae: 3724.8391\n",
            "Epoch 167/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3722.2410 - mae: 3722.2410\n",
            "Epoch 168/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3719.0071 - mae: 3719.0071\n",
            "Epoch 169/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3719.2148 - mae: 3719.2148\n",
            "Epoch 170/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3712.8230 - mae: 3712.8230\n",
            "Epoch 171/200\n",
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            "Epoch 172/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3712.9204 - mae: 3712.9204\n",
            "Epoch 173/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3709.7498 - mae: 3709.7498\n",
            "Epoch 174/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3713.0569 - mae: 3713.0569\n",
            "Epoch 175/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3715.2971 - mae: 3715.2971\n",
            "Epoch 176/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3710.8982 - mae: 3710.8982\n",
            "Epoch 177/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3704.7893 - mae: 3704.7893\n",
            "Epoch 178/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3701.6223 - mae: 3701.6223\n",
            "Epoch 179/200\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3712.0498 - mae: 3712.0498\n",
            "Epoch 180/200\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 3714.6211 - mae: 3714.6211\n",
            "Epoch 181/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3700.8477 - mae: 3700.8477\n",
            "Epoch 182/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3698.5808 - mae: 3698.5808\n",
            "Epoch 183/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3697.5549 - mae: 3697.5549\n",
            "Epoch 184/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3702.1711 - mae: 3702.1711\n",
            "Epoch 185/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3697.2859 - mae: 3697.2859\n",
            "Epoch 186/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3699.6304 - mae: 3699.6304\n",
            "Epoch 187/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3693.4404 - mae: 3693.4404\n",
            "Epoch 188/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3698.7354 - mae: 3698.7354\n",
            "Epoch 189/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3689.0247 - mae: 3689.0247\n",
            "Epoch 190/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3688.8276 - mae: 3688.8276\n",
            "Epoch 191/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3703.6494 - mae: 3703.6494\n",
            "Epoch 192/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3690.0498 - mae: 3690.0498\n",
            "Epoch 193/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3679.0645 - mae: 3679.0645\n",
            "Epoch 194/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3679.3638 - mae: 3679.3638\n",
            "Epoch 195/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3678.8328 - mae: 3678.8328\n",
            "Epoch 196/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3686.0217 - mae: 3686.0217\n",
            "Epoch 197/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3670.9858 - mae: 3670.9858\n",
            "Epoch 198/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3676.4888 - mae: 3676.4888\n",
            "Epoch 199/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3685.3689 - mae: 3685.3689\n",
            "Epoch 200/200\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3671.6958 - mae: 3671.6958\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the third model\n",
        "insurance_model_3.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ORSii1vWSSBR",
        "outputId": "9fdfc776-f9c9-49c1-e2cd-83bfc49e773a"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 4ms/step - loss: 3503.1758 - mae: 3503.1758\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3503.17578125, 3503.17578125]"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot history (also known as the loss curve or training curve)\n",
        "pd.DataFrame(history.history).plot()\n",
        "plt.ylabel(\"loss\")\n",
        "plt.xlabel(\"epochs\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "tlOIUojBScLF",
        "outputId": "c4bc2fa7-5c6a-4c53-b46d-ef4337ecdffe"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing Data (normalization and standardization)\n",
        "\n",
        "In terms of scaling values, neural networks tends to prefer normalization."
      ],
      "metadata": {
        "id": "22bc7YZ7S_CU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "# Read the insurance DataFrame\n",
        "insurance = pd.read_csv(\"https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv\")\n",
        "insurance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "HNHUS2n4YFiX",
        "outputId": "f10bc24a-701a-429e-9ab2-f8b575f18560"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age     sex     bmi  children smoker     region      charges\n",
              "0      19  female  27.900         0    yes  southwest  16884.92400\n",
              "1      18    male  33.770         1     no  southeast   1725.55230\n",
              "2      28    male  33.000         3     no  southeast   4449.46200\n",
              "3      33    male  22.705         0     no  northwest  21984.47061\n",
              "4      32    male  28.880         0     no  northwest   3866.85520\n",
              "...   ...     ...     ...       ...    ...        ...          ...\n",
              "1333   50    male  30.970         3     no  northwest  10600.54830\n",
              "1334   18  female  31.920         0     no  northeast   2205.98080\n",
              "1335   18  female  36.850         0     no  southeast   1629.83350\n",
              "1336   21  female  25.800         0     no  southwest   2007.94500\n",
              "1337   61  female  29.070         0    yes  northwest  29141.36030\n",
              "\n",
              "[1338 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3a8bc08-57dd-47e3-ac6d-8eda16926368\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1333</th>\n",
              "      <td>50</td>\n",
              "      <td>male</td>\n",
              "      <td>30.970</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>10600.54830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northeast</td>\n",
              "      <td>2205.98080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>female</td>\n",
              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1629.83350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>21</td>\n",
              "      <td>female</td>\n",
              "      <td>25.800</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>southwest</td>\n",
              "      <td>2007.94500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>29.070</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>northwest</td>\n",
              "      <td>29141.36030</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3a8bc08-57dd-47e3-ac6d-8eda16926368')\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert: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",
              "      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",
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              "        document.querySelector('#df-b3a8bc08-57dd-47e3-ac6d-8eda16926368 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-b3a8bc08-57dd-47e3-ac6d-8eda16926368');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: var(--fill-color);\n",
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              "    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",
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              "\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-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-6a0388a7-9dc2-4757-83d1-d26876193189 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Create a column transformer\n",
        "ct =  make_column_transformer(\n",
        "    (MinMaxScaler(), [\"age\", \"bmi\", \"children\"]), # Turning all values in these columns between 0-1\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",
        "# Building train and test splits\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
        "\n",
        "# Fit the column transformer to the training data\n",
        "ct.fit(X_train)\n",
        "\n",
        "# Transform training and testing data with normalization (MinMaxScalar) and OneHotEncoder\n",
        "X_train_normal = ct.transform(X_train)\n",
        "X_test_normal = ct.transform(X_test)"
      ],
      "metadata": {
        "id": "knjoBGz4Y5pb"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.loc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VwoDFer9bdhf",
        "outputId": "856faff6-5a3a-472b-d76c-f48b60e632f9"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                19\n",
              "sex            female\n",
              "bmi              27.9\n",
              "children            0\n",
              "smoker            yes\n",
              "region      southwest\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_normal[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q092sybJZcym",
        "outputId": "e263eafe-050b-44d2-8780-e7a0728cb367"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.60869565, 0.10734463, 0.4       , 1.        , 0.        ,\n",
              "       1.        , 0.        , 0.        , 1.        , 0.        ,\n",
              "       0.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape ,X_train_normal.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zpd5Xxf1bb58",
        "outputId": "7545cf37-aa80-48b0-88f5-0c0180dd1db0"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1070, 6), (1070, 11))"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a neural network model to fit on the normalized data\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create a model\n",
        "insurance_model_4 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# 2. Compile the model\n",
        "insurance_model_4.compile(loss = tf.keras.losses.mae,\n",
        "                          optimizer = tf.keras.optimizers.Adam(),\n",
        "                          metrics = [\"mae\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "insurance_model_4.fit(X_train_normal, y_train, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VDkdqw3bhxH",
        "outputId": "401cbc98-f31c-4575-8983-f22f77f54bcd"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 3s 6ms/step - loss: 13344.7559 - mae: 13344.7559\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 13339.0391 - mae: 13339.0391\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 13323.3330 - mae: 13323.3330\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 13288.0869 - mae: 13288.0869\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 13222.6230 - mae: 13222.6230\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 13115.5586 - mae: 13115.5586\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 12956.0010 - mae: 12956.0010\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 12733.4814 - mae: 12733.4814\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 12437.5254 - mae: 12437.5254\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 12059.8184 - mae: 12059.8184\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 11605.3262 - mae: 11605.3262\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 11098.9492 - mae: 11098.9492\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 10594.5732 - mae: 10594.5732\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 10092.7705 - mae: 10092.7705\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 9612.8418 - mae: 9612.8418\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 9174.4912 - mae: 9174.4912\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 8805.3809 - mae: 8805.3809\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 8508.2227 - mae: 8508.2227\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 8276.7061 - mae: 8276.7061\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 10ms/step - loss: 8117.9028 - mae: 8117.9028\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 7999.0391 - mae: 7999.0391\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7916.1777 - mae: 7916.1777\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7854.3208 - mae: 7854.3208\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7800.6938 - mae: 7800.6938\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7762.1128 - mae: 7762.1128\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7711.0029 - mae: 7711.0029\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7669.9292 - mae: 7669.9292\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7628.4458 - mae: 7628.4458\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7586.6724 - mae: 7586.6724\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7544.3750 - mae: 7544.3750\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7501.8208 - mae: 7501.8208\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7459.0264 - mae: 7459.0264\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7416.0811 - mae: 7416.0811\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 7369.4351 - mae: 7369.4351\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7324.3101 - mae: 7324.3101\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7276.0044 - mae: 7276.0044\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7227.4688 - mae: 7227.4688\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7178.2988 - mae: 7178.2988\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7125.6265 - mae: 7125.6265\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 7072.4521 - mae: 7072.4521\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7018.0771 - mae: 7018.0771\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6961.8540 - mae: 6961.8540\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6901.8618 - mae: 6901.8618\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6838.6831 - mae: 6838.6831\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6774.7651 - mae: 6774.7651\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6708.7891 - mae: 6708.7891\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6641.6548 - mae: 6641.6548\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6566.5718 - mae: 6566.5718\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6491.8535 - mae: 6491.8535\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6410.2676 - mae: 6410.2676\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6330.8662 - mae: 6330.8662\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6242.1401 - mae: 6242.1401\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6151.4951 - mae: 6151.4951\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6058.3457 - mae: 6058.3457\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5956.6904 - mae: 5956.6904\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5854.2583 - mae: 5854.2583\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5744.5020 - mae: 5744.5020\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5630.1079 - mae: 5630.1079\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5515.9971 - mae: 5515.9971\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5400.3037 - mae: 5400.3037\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5277.1211 - mae: 5277.1211\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5152.6455 - mae: 5152.6455\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5027.9517 - mae: 5027.9517\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4897.0605 - mae: 4897.0605\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4769.9902 - mae: 4769.9902\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4640.4482 - mae: 4640.4482\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4520.6050 - mae: 4520.6050\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4406.4712 - mae: 4406.4712\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4300.5166 - mae: 4300.5166\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4200.0156 - mae: 4200.0156\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4105.9692 - mae: 4105.9692\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4020.0701 - mae: 4020.0701\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3943.8823 - mae: 3943.8823\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3878.1816 - mae: 3878.1816\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3826.7478 - mae: 3826.7478\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3784.8181 - mae: 3784.8181\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 3750.3655 - mae: 3750.3655\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3726.0847 - mae: 3726.0847\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3707.1287 - mae: 3707.1287\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3694.3416 - mae: 3694.3416\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3685.5625 - mae: 3685.5625\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3680.3750 - mae: 3680.3750\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3672.4167 - mae: 3672.4167\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3668.7317 - mae: 3668.7317\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3666.0964 - mae: 3666.0964\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3663.1316 - mae: 3663.1316\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3660.0359 - mae: 3660.0359\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3657.4199 - mae: 3657.4199\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3655.4534 - mae: 3655.4534\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3653.8391 - mae: 3653.8391\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3651.0098 - mae: 3651.0098\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3650.0869 - mae: 3650.0869\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3647.9014 - mae: 3647.9014\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3646.2732 - mae: 3646.2732\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3647.2827 - mae: 3647.2827\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3644.1780 - mae: 3644.1780\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3641.7751 - mae: 3641.7751\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3639.7295 - mae: 3639.7295\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3639.1438 - mae: 3639.1438\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3637.6851 - mae: 3637.6851\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e42eefedba0>"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the insurance model trained on normalized data\n",
        "insurance_model_4.evaluate(X_test_normal, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z3LUn0A7cXyP",
        "outputId": "d1c4f637-3d4f-422a-8bce-bf79665bace9"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 4ms/step - loss: 3442.8350 - mae: 3442.8350\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3442.8349609375, 3442.8349609375]"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lYHaVUWrcjwi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}