02_neural_network_with_tf.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Inroduction to regression with neural network in TensorFlow\n",
        "\n",
        "There are many definitions for regression problems but in out case, we're going to simplify it: predicting a numerical variable based on some other combination of variables, even shorter... predicting a number."
      ],
      "metadata": {
        "id": "s0CRVw1fC1sJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "pm65Ik61xrLG",
        "outputId": "e1f5528b-fa14-4198-fcfc-11c9af563c45"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.13.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 133
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating a data to View and Fit"
      ],
      "metadata": {
        "id": "M3tjyZ8-DdZG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "#Visualize it\n",
        "plt.scatter(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "RyzOjd99DhUM",
        "outputId": "97ab8896-9e3c-4c49-c530-de899a9c24e3"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7b16871552d0>"
            ]
          },
          "metadata": {},
          "execution_count": 134
        },
        {
          "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": [
        "y == X + 10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRDiADmNEMRB",
        "outputId": "7d3dc740-0b19-44e9-99bd-d1c7293e6738"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True,  True,  True,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Input and output shapes"
      ],
      "metadata": {
        "id": "G1ZdUry6ESKE"
      }
    },
    {
      "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\n",
        "# Ip shape is 3 and Op shape is 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kg9dlJNwEPfF",
        "outputId": "d0ac4424-c897-48ed-e9e2-91986b10ecad"
      },
      "execution_count": 136,
      "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": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X[0],y[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_bY6JYPEE9zR",
        "outputId": "453a6c4c-181d-431e-cc23-4e3a5e642e18"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-7.0, 3.0)"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ip_shape = X.shape\n",
        "op_shape = y.shape\n",
        "ip_shape,op_shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IO7JYfiZEw9F",
        "outputId": "b0af9918-1e25-4458-8759-5d5bec0fb9c7"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8,), (8,))"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn our numpy arrays into tensors with dtype float32 - ip should be in tensors\n",
        "X = tf.cast(tf.constant(X), dtype = tf.float32)\n",
        "y = tf.cast(tf.constant(y), dtype = tf.float32)\n",
        "X, y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o9exFbPXFS5i",
        "outputId": "8e21169d-7227-4b59-eb34-42e4ec8170e3"
      },
      "execution_count": 139,
      "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": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Steps in modelling with TensorFlow**\n",
        "1. **Creating a model** - define the ip and op layers, as well as the hidden layers of a DL model.\n",
        "2. **Compiling a model** - define the loss function (in other words, the function which tell our model how wrong it is) and the optimizer (tells our model how to improve the patterns its learning) and evaluation metrics (what we can use to interpret the performance of our model).\n",
        "3. **Fitting the model** - letting the model try to find patterns between X & y (features and labels)"
      ],
      "metadata": {
        "id": "u6KVGcbtGDn_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create a model using the Sequential API\n",
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1) #hidden layer - 1 neuron\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss=tf.keras.losses.mae, # mae is short for mean absolute error\n",
        "              optimizer=tf.keras.optimizers.SGD(), # SGD is short for stochastic gradient descent\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "# Fit the model\n",
        "# model.fit(X, y, epochs=5) # this will break with TensorFlow 2.7.0+\n",
        "model.fit(tf.expand_dims(X, axis=1), y, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ytaZsJmfF4DK",
        "outputId": "58d0f596-fe8c-4e1a-a966-13aa60dd648d"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1/1 [==============================] - 0s 311ms/step - loss: 8.1319 - mae: 8.1319\n",
            "Epoch 2/5\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.9994 - mae: 7.9994\n",
            "Epoch 3/5\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.8669 - mae: 7.8669\n",
            "Epoch 4/5\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.7344 - mae: 7.7344\n",
            "Epoch 5/5\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.6019 - mae: 7.6019\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b171c3a94e0>"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out X and y\n",
        "X,y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xTn-GS2FQMhJ",
        "outputId": "757fd032-7ff2-4e68-e9a0-75f2afd25a19"
      },
      "execution_count": 141,
      "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": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Try and make prediction using our model\n",
        "y_pred = model.predict([17.0]) # X=17 so op should be y = x+10 that is 27\n",
        "y_pred\n",
        "#gives a far off op"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "msSZBYb9QdJE",
        "outputId": "2482e284-c227-448f-bf9c-16ff33ae204a"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 55ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[29.098547]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Improving our model\n",
        "We can improve our model, by altering the steps we took to create a model\n",
        "\n",
        "1. **Creating a model:** here we might add more layers, increase the no. of hidden unites (all called neurons) within each of the hidde layers, change the activiation fucntion of each layer.\n",
        "\n",
        "2. **Compiling a model:** here we might change the optimization function or perhaps the learning rate of the optimization function.\n",
        "\n",
        "3. **Fitting a model:**here we might fit a model for more epochs (leave it training for longer) or on more data (give model more examples to learn from)."
      ],
      "metadata": {
        "id": "nKnf7_1HRgmL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Create a model\n",
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(1) #hidden layer - 1 neuron\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss=tf.keras.losses.mae, # mae is short for mean absolute error\n",
        "              optimizer=tf.keras.optimizers.SGD(), # SGD is short for stochastic gradient descent\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "# Fit the model\n",
        "# model.fit(X, y, epochs=5) # this will break with TensorFlow 2.7.0+\n",
        "model.fit(tf.expand_dims(X, axis=1), y, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gWhPSE5Q33A",
        "outputId": "1c50a518-6487-4015-cce4-a8896eeb2860"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 1s 628ms/step - loss: 15.5123 - mae: 15.5123\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 15.2311 - mae: 15.2311\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 14.9590 - mae: 14.9590\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 14.8265 - mae: 14.8265\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 14.6940 - mae: 14.6940\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 14.5615 - mae: 14.5615\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 14.4290 - mae: 14.4290\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 14.2965 - mae: 14.2965\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 14.1640 - mae: 14.1640\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 23ms/step - loss: 14.0315 - mae: 14.0315\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 13.8990 - mae: 13.8990\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 13.7665 - mae: 13.7665\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 13.6340 - mae: 13.6340\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 13.5015 - mae: 13.5015\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 13.3690 - mae: 13.3690\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 13.2365 - mae: 13.2365\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 13.1040 - mae: 13.1040\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 12.9715 - mae: 12.9715\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 12.8390 - mae: 12.8390\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 12.7065 - mae: 12.7065\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 12.5740 - mae: 12.5740\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 12.4415 - mae: 12.4415\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 12.3090 - mae: 12.3090\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 12.1765 - mae: 12.1765\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 12.0440 - mae: 12.0440\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 11.9115 - mae: 11.9115\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 11.7790 - mae: 11.7790\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 11.6465 - mae: 11.6465\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 11.5140 - mae: 11.5140\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 11.3815 - mae: 11.3815\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 11.2490 - mae: 11.2490\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 11.1165 - mae: 11.1165\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 10.9840 - mae: 10.9840\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 10.8515 - mae: 10.8515\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.7190 - mae: 10.7190\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 10.5865 - mae: 10.5865\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 10.4540 - mae: 10.4540\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.3215 - mae: 10.3215\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 10.1890 - mae: 10.1890\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 10.0565 - mae: 10.0565\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.9240 - mae: 9.9240\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.7915 - mae: 9.7915\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.6590 - mae: 9.6590\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.5265 - mae: 9.5265\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.3940 - mae: 9.3940\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.2615 - mae: 9.2615\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 9.1290 - mae: 9.1290\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.9965 - mae: 8.9965\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 8.8640 - mae: 8.8640\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 8.7315 - mae: 8.7315\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 8.5990 - mae: 8.5990\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 8.4665 - mae: 8.4665\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.3340 - mae: 8.3340\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.2015 - mae: 8.2015\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.0690 - mae: 8.0690\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.9365 - mae: 7.9365\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.8040 - mae: 7.8040\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.6715 - mae: 7.6715\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.5390 - mae: 7.5390\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.4065 - mae: 7.4065\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.2740 - mae: 7.2740\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.1415 - mae: 7.1415\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.0388 - mae: 7.0388\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.0331 - mae: 7.0331\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.0275 - mae: 7.0275\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.0219 - mae: 7.0219\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.0163 - mae: 7.0163\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.0106 - mae: 7.0106\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.0050 - mae: 7.0050\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9994 - mae: 6.9994\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9938 - mae: 6.9938\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9881 - mae: 6.9881\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.9825 - mae: 6.9825\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 6.9769 - mae: 6.9769\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9713 - mae: 6.9713\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 6.9656 - mae: 6.9656\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9600 - mae: 6.9600\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9544 - mae: 6.9544\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9488 - mae: 6.9488\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9431 - mae: 6.9431\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9375 - mae: 6.9375\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9319 - mae: 6.9319\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9262 - mae: 6.9262\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9206 - mae: 6.9206\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9150 - mae: 6.9150\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.9094 - mae: 6.9094\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 6.9038 - mae: 6.9038\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8981 - mae: 6.8981\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8925 - mae: 6.8925\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8869 - mae: 6.8869\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8813 - mae: 6.8813\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 6.8756 - mae: 6.8756\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8700 - mae: 6.8700\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8644 - mae: 6.8644\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 6.8588 - mae: 6.8588\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 6.8531 - mae: 6.8531\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.8475 - mae: 6.8475\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8419 - mae: 6.8419\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8363 - mae: 6.8363\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.8306 - mae: 6.8306\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b1673673bb0>"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now let's see if model's prediction has improved\n",
        "model.predict([17.0])\n",
        "# see the difference between epochs 5 and 100. we came close to the prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwI7HUdwx_zA",
        "outputId": "23138007-3b78-4376-ad17-45153d96d4d3"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 51ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[29.440365]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's see if we can make another change to improve the model\n",
        "\n",
        "# 1. Create the model (this time with extra hidden layer with 100 hidden units)\n",
        "model = tf.keras.Sequential([tf.keras.layers.Dense(100,activation=None),\n",
        "        tf.keras.layers.Dense(1)])\n",
        "\n",
        "# 2.Compile the model\n",
        "model.compile(loss=\"mae\",\n",
        "              optimizer = tf.keras.optimizers.Adam(lr=0.1),\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "# 3.Fit the model\n",
        "model.fit(tf.expand_dims(X, axis=-1), y, epochs=300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uv4dBx-XyKKT",
        "outputId": "a9b51909-0b06-49db-e56f-9bed210782db"
      },
      "execution_count": 145,
      "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 1/300\n",
            "1/1 [==============================] - 0s 429ms/step - loss: 13.3250 - mae: 13.3250\n",
            "Epoch 2/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 13.2339 - mae: 13.2339\n",
            "Epoch 3/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 13.1429 - mae: 13.1429\n",
            "Epoch 4/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 13.0520 - mae: 13.0520\n",
            "Epoch 5/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 12.9610 - mae: 12.9610\n",
            "Epoch 6/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 12.8701 - mae: 12.8701\n",
            "Epoch 7/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 12.7791 - mae: 12.7791\n",
            "Epoch 8/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 12.6880 - mae: 12.6880\n",
            "Epoch 9/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 12.5969 - mae: 12.5969\n",
            "Epoch 10/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 12.5057 - mae: 12.5057\n",
            "Epoch 11/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 12.4144 - mae: 12.4144\n",
            "Epoch 12/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 12.3229 - mae: 12.3229\n",
            "Epoch 13/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 12.2314 - mae: 12.2314\n",
            "Epoch 14/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 12.1397 - mae: 12.1397\n",
            "Epoch 15/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 12.0478 - mae: 12.0478\n",
            "Epoch 16/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 11.9558 - mae: 11.9558\n",
            "Epoch 17/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 11.8636 - mae: 11.8636\n",
            "Epoch 18/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 11.7712 - mae: 11.7712\n",
            "Epoch 19/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 11.6786 - mae: 11.6786\n",
            "Epoch 20/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 11.5857 - mae: 11.5857\n",
            "Epoch 21/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 11.4926 - mae: 11.4926\n",
            "Epoch 22/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 11.3991 - mae: 11.3991\n",
            "Epoch 23/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 11.3054 - mae: 11.3054\n",
            "Epoch 24/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 11.2113 - mae: 11.2113\n",
            "Epoch 25/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 11.1169 - mae: 11.1169\n",
            "Epoch 26/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 11.0221 - mae: 11.0221\n",
            "Epoch 27/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.9268 - mae: 10.9268\n",
            "Epoch 28/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.8312 - mae: 10.8312\n",
            "Epoch 29/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.7351 - mae: 10.7351\n",
            "Epoch 30/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.6385 - mae: 10.6385\n",
            "Epoch 31/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 10.5414 - mae: 10.5414\n",
            "Epoch 32/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.4438 - mae: 10.4438\n",
            "Epoch 33/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.3457 - mae: 10.3457\n",
            "Epoch 34/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.2469 - mae: 10.2469\n",
            "Epoch 35/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 10.1476 - mae: 10.1476\n",
            "Epoch 36/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 10.0477 - mae: 10.0477\n",
            "Epoch 37/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.9471 - mae: 9.9471\n",
            "Epoch 38/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.8458 - mae: 9.8458\n",
            "Epoch 39/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.7439 - mae: 9.7439\n",
            "Epoch 40/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.6413 - mae: 9.6413\n",
            "Epoch 41/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 9.5379 - mae: 9.5379\n",
            "Epoch 42/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.4338 - mae: 9.4338\n",
            "Epoch 43/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 9.3289 - mae: 9.3289\n",
            "Epoch 44/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 9.2233 - mae: 9.2233\n",
            "Epoch 45/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.1168 - mae: 9.1168\n",
            "Epoch 46/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 9.0095 - mae: 9.0095\n",
            "Epoch 47/300\n",
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            "Epoch 48/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 8.7924 - mae: 8.7924\n",
            "Epoch 49/300\n",
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            "Epoch 50/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.5718 - mae: 8.5718\n",
            "Epoch 51/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 8.4601 - mae: 8.4601\n",
            "Epoch 52/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 8.3475 - mae: 8.3475\n",
            "Epoch 53/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 8.2339 - mae: 8.2339\n",
            "Epoch 54/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.1193 - mae: 8.1193\n",
            "Epoch 55/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 8.0038 - mae: 8.0038\n",
            "Epoch 56/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.8872 - mae: 7.8872\n",
            "Epoch 57/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.7697 - mae: 7.7697\n",
            "Epoch 58/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.6511 - mae: 7.6511\n",
            "Epoch 59/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.5314 - mae: 7.5314\n",
            "Epoch 60/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.4107 - mae: 7.4107\n",
            "Epoch 61/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.2888 - mae: 7.2888\n",
            "Epoch 62/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.1659 - mae: 7.1659\n",
            "Epoch 63/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 7.0418 - mae: 7.0418\n",
            "Epoch 64/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 6.9166 - mae: 6.9166\n",
            "Epoch 65/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 6.7902 - mae: 6.7902\n",
            "Epoch 66/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.7595 - mae: 6.7595\n",
            "Epoch 67/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.7440 - mae: 6.7440\n",
            "Epoch 68/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.7288 - mae: 6.7288\n",
            "Epoch 69/300\n",
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            "Epoch 70/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.6991 - mae: 6.6991\n",
            "Epoch 71/300\n",
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            "Epoch 72/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 6.6704 - mae: 6.6704\n",
            "Epoch 73/300\n",
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            "Epoch 74/300\n",
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            "Epoch 76/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 6.7456 - mae: 6.7456\n",
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            "Epoch 78/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.7318 - mae: 6.7318\n",
            "Epoch 79/300\n",
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            "Epoch 80/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 6.6910 - mae: 6.6910\n",
            "Epoch 81/300\n",
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            "Epoch 82/300\n",
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            "Epoch 89/300\n",
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            "Epoch 102/300\n",
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            "Epoch 107/300\n",
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            "Epoch 108/300\n",
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            "Epoch 124/300\n",
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            "Epoch 127/300\n",
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            "Epoch 128/300\n",
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            "Epoch 129/300\n",
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            "Epoch 131/300\n",
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            "Epoch 140/300\n",
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            "Epoch 141/300\n",
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            "Epoch 142/300\n",
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            "Epoch 147/300\n",
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            "Epoch 148/300\n",
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            "Epoch 150/300\n",
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            "Epoch 151/300\n",
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            "Epoch 152/300\n",
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            "Epoch 153/300\n",
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            "Epoch 154/300\n",
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            "Epoch 155/300\n",
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            "Epoch 156/300\n",
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            "Epoch 157/300\n",
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            "Epoch 158/300\n",
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            "Epoch 159/300\n",
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            "Epoch 160/300\n",
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            "Epoch 161/300\n",
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            "Epoch 162/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.4505 - mae: 5.4505\n",
            "Epoch 163/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.4312 - mae: 5.4312\n",
            "Epoch 164/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.4118 - mae: 5.4118\n",
            "Epoch 165/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.3991 - mae: 5.3991\n",
            "Epoch 166/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.3782 - mae: 5.3782\n",
            "Epoch 167/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.3564 - mae: 5.3564\n",
            "Epoch 168/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.3387 - mae: 5.3387\n",
            "Epoch 169/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.3205 - mae: 5.3205\n",
            "Epoch 170/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.3019 - mae: 5.3019\n",
            "Epoch 171/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.2829 - mae: 5.2829\n",
            "Epoch 172/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.2635 - mae: 5.2635\n",
            "Epoch 173/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.2438 - mae: 5.2438\n",
            "Epoch 174/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.2237 - mae: 5.2237\n",
            "Epoch 175/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.2033 - mae: 5.2033\n",
            "Epoch 176/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.1825 - mae: 5.1825\n",
            "Epoch 177/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.1615 - mae: 5.1615\n",
            "Epoch 178/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.1401 - mae: 5.1401\n",
            "Epoch 179/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.1185 - mae: 5.1185\n",
            "Epoch 180/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.0966 - mae: 5.0966\n",
            "Epoch 181/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.0823 - mae: 5.0823\n",
            "Epoch 182/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.0579 - mae: 5.0579\n",
            "Epoch 183/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 5.0341 - mae: 5.0341\n",
            "Epoch 184/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.0140 - mae: 5.0140\n",
            "Epoch 185/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 4.9934 - mae: 4.9934\n",
            "Epoch 186/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 4.9723 - mae: 4.9723\n",
            "Epoch 187/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 4.9508 - mae: 4.9508\n",
            "Epoch 188/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.9288 - mae: 4.9288\n",
            "Epoch 189/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 4.9065 - mae: 4.9065\n",
            "Epoch 190/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 4.8837 - mae: 4.8837\n",
            "Epoch 191/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 4.8606 - mae: 4.8606\n",
            "Epoch 192/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 4.8371 - mae: 4.8371\n",
            "Epoch 193/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 4.8132 - mae: 4.8132\n",
            "Epoch 194/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 4.7891 - mae: 4.7891\n",
            "Epoch 195/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.7646 - mae: 4.7646\n",
            "Epoch 196/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.7398 - mae: 4.7398\n",
            "Epoch 197/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 4.7157 - mae: 4.7157\n",
            "Epoch 198/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 4.6911 - mae: 4.6911\n",
            "Epoch 199/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 4.6670 - mae: 4.6670\n",
            "Epoch 200/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 4.6425 - mae: 4.6425\n",
            "Epoch 201/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 4.6176 - mae: 4.6176\n",
            "Epoch 202/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.5922 - mae: 4.5922\n",
            "Epoch 203/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.5707 - mae: 4.5707\n",
            "Epoch 204/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 4.5423 - mae: 4.5423\n",
            "Epoch 205/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.5175 - mae: 4.5175\n",
            "Epoch 206/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 4.4923 - mae: 4.4923\n",
            "Epoch 207/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.4666 - mae: 4.4666\n",
            "Epoch 208/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.4404 - mae: 4.4404\n",
            "Epoch 209/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.4138 - mae: 4.4138\n",
            "Epoch 210/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.3868 - mae: 4.3868\n",
            "Epoch 211/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.3594 - mae: 4.3594\n",
            "Epoch 212/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.3316 - mae: 4.3316\n",
            "Epoch 213/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.3105 - mae: 4.3105\n",
            "Epoch 214/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.2769 - mae: 4.2769\n",
            "Epoch 215/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.2498 - mae: 4.2498\n",
            "Epoch 216/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 4.2222 - mae: 4.2222\n",
            "Epoch 217/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.1941 - mae: 4.1941\n",
            "Epoch 218/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.1656 - mae: 4.1656\n",
            "Epoch 219/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.1367 - mae: 4.1367\n",
            "Epoch 220/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.1088 - mae: 4.1088\n",
            "Epoch 221/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.0795 - mae: 4.0795\n",
            "Epoch 222/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.0511 - mae: 4.0511\n",
            "Epoch 223/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.0222 - mae: 4.0222\n",
            "Epoch 224/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.9927 - mae: 3.9927\n",
            "Epoch 225/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.9628 - mae: 3.9628\n",
            "Epoch 226/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.9324 - mae: 3.9324\n",
            "Epoch 227/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.9016 - mae: 3.9016\n",
            "Epoch 228/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.8704 - mae: 3.8704\n",
            "Epoch 229/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.8387 - mae: 3.8387\n",
            "Epoch 230/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.8067 - mae: 3.8067\n",
            "Epoch 231/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7826 - mae: 3.7826\n",
            "Epoch 232/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 3.7440 - mae: 3.7440\n",
            "Epoch 233/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.7141 - mae: 3.7141\n",
            "Epoch 234/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.6841 - mae: 3.6841\n",
            "Epoch 235/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.6534 - mae: 3.6534\n",
            "Epoch 236/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.6220 - mae: 3.6220\n",
            "Epoch 237/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.5901 - mae: 3.5901\n",
            "Epoch 238/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.5576 - mae: 3.5576\n",
            "Epoch 239/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.5245 - mae: 3.5245\n",
            "Epoch 240/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.4910 - mae: 3.4910\n",
            "Epoch 241/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.4569 - mae: 3.4569\n",
            "Epoch 242/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.4224 - mae: 3.4224\n",
            "Epoch 243/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.3875 - mae: 3.3875\n",
            "Epoch 244/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.3521 - mae: 3.3521\n",
            "Epoch 245/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.3162 - mae: 3.3162\n",
            "Epoch 246/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.2800 - mae: 3.2800\n",
            "Epoch 247/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.2434 - mae: 3.2434\n",
            "Epoch 248/300\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 3.2064 - mae: 3.2064\n",
            "Epoch 249/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.1690 - mae: 3.1690\n",
            "Epoch 250/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.1313 - mae: 3.1313\n",
            "Epoch 251/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.1003 - mae: 3.1003\n",
            "Epoch 252/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.0585 - mae: 3.0585\n",
            "Epoch 253/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.0221 - mae: 3.0221\n",
            "Epoch 254/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.9866 - mae: 2.9866\n",
            "Epoch 255/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.9504 - mae: 2.9504\n",
            "Epoch 256/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.9136 - mae: 2.9136\n",
            "Epoch 257/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.8762 - mae: 2.8762\n",
            "Epoch 258/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.8382 - mae: 2.8382\n",
            "Epoch 259/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.7996 - mae: 2.7996\n",
            "Epoch 260/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.7605 - mae: 2.7605\n",
            "Epoch 261/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.7209 - mae: 2.7209\n",
            "Epoch 262/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.6808 - mae: 2.6808\n",
            "Epoch 263/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.6402 - mae: 2.6402\n",
            "Epoch 264/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.5992 - mae: 2.5992\n",
            "Epoch 265/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.5577 - mae: 2.5577\n",
            "Epoch 266/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.5158 - mae: 2.5158\n",
            "Epoch 267/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.4735 - mae: 2.4735\n",
            "Epoch 268/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.4308 - mae: 2.4308\n",
            "Epoch 269/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.3877 - mae: 2.3877\n",
            "Epoch 270/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.3484 - mae: 2.3484\n",
            "Epoch 271/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.3025 - mae: 2.3025\n",
            "Epoch 272/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.2603 - mae: 2.2603\n",
            "Epoch 273/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.2174 - mae: 2.2174\n",
            "Epoch 274/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.1741 - mae: 2.1741\n",
            "Epoch 275/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.1302 - mae: 2.1302\n",
            "Epoch 276/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.0859 - mae: 2.0859\n",
            "Epoch 277/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.0431 - mae: 2.0431\n",
            "Epoch 278/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 1.9980 - mae: 1.9980\n",
            "Epoch 279/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 1.9542 - mae: 1.9542\n",
            "Epoch 280/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 1.9099 - mae: 1.9099\n",
            "Epoch 281/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 1.8649 - mae: 1.8649\n",
            "Epoch 282/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 1.8194 - mae: 1.8194\n",
            "Epoch 283/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 1.7733 - mae: 1.7733\n",
            "Epoch 284/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 1.7267 - mae: 1.7267\n",
            "Epoch 285/300\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.6795 - mae: 1.6795\n",
            "Epoch 286/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 1.6319 - mae: 1.6319\n",
            "Epoch 287/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.5838 - mae: 1.5838\n",
            "Epoch 288/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.5352 - mae: 1.5352\n",
            "Epoch 289/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.4861 - mae: 1.4861\n",
            "Epoch 290/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 1.4428 - mae: 1.4428\n",
            "Epoch 291/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 1.3891 - mae: 1.3891\n",
            "Epoch 292/300\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.3409 - mae: 1.3409\n",
            "Epoch 293/300\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 1.2921 - mae: 1.2921\n",
            "Epoch 294/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 1.2426 - mae: 1.2426\n",
            "Epoch 295/300\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.1926 - mae: 1.1926\n",
            "Epoch 296/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.1421 - mae: 1.1421\n",
            "Epoch 297/300\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.0911 - mae: 1.0911\n",
            "Epoch 298/300\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.0395 - mae: 1.0395\n",
            "Epoch 299/300\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.9874 - mae: 0.9874\n",
            "Epoch 300/300\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.9364 - mae: 0.9364\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b166f8f46d0>"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([17.0])\n",
        "# From activation = \"relu\", we changed it to none. hence we get 29.1,\n",
        "# else it would  be 31\n",
        "\n",
        "# Note: improving a model dont always result in a better model\n",
        "\n",
        "# we got 27.5 - close enough"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Vbz-AMA0OBM",
        "outputId": "3001ae37-70fc-4362-e175-fbb2f0256afe"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 70ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[27.549406]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluating a model\n",
        "in practice, a typical workflow you'll go through when building neural network is:\n",
        "\n",
        "`build a model -> fit it -> evaluate it -> tweak a model -> fit it -> evaluate it`\n",
        "\n",
        "when it comes to evaluation, `visualization` is very important\n",
        "\n",
        "* What `data` are we working with? what does it look like?\n",
        "* what does our `model` look like?\n",
        "* The `training` of a model - how does a model perform while it learns?\n",
        "* How do the `predictions` of the model line up against the ground truth (the original labels)?"
      ],
      "metadata": {
        "id": "_JIbjtBW5rUP"
      }
    },
    {
      "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": "LWLw-1oW2aq6",
        "outputId": "08a29a40-3e89-4bdb-fdf0-ad4e648ce8ef"
      },
      "execution_count": 147,
      "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": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make labels for the dataset\n",
        "y = X+10\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ioC8n-Ru7AsN",
        "outputId": "5de6a205-f7ed-4dc4-d817-9e4948121a60"
      },
      "execution_count": 148,
      "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": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "smRt-u1G7FTb",
        "outputId": "fcde5d5f-5550-421f-d03a-6c80b530cf65"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7b166f91a890>"
            ]
          },
          "metadata": {},
          "execution_count": 149
        },
        {
          "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": [
        "# Concept of 2 Sets\n",
        "80% - training set and 20% - testing set"
      ],
      "metadata": {
        "id": "Vg3W8-VE7do6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AzD8f92w7pDn",
        "outputId": "4014aeac-03a0-4a4e-82d8-d2bec7383ac4"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into train and test sets\n",
        "X_train = X[:40] #first 40 samples out of 50\n",
        "y_train = y[:40]\n",
        "\n",
        "X_test = X[40:]\n",
        "y_test = y[40:]\n",
        "len(X_train),len(X_test),len(y_train),len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDNB9v9LAli1",
        "outputId": "57966d21-361f-418f-9578-450f4e4b0eb9"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 10, 40, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "plt.scatter(X_train,y_train,c=\"r\",label = \"Training data\")\n",
        "plt.scatter(X_test,y_test,c=\"b\",label = \"Testing data\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "v2K1Q3i4CpIS",
        "outputId": "096d3b61-15dd-413f-df23-9fb4e80be2de"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7b166efff550>"
            ]
          },
          "metadata": {},
          "execution_count": 152
        },
        {
          "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 another neural network for our data\n",
        "# 1.Create a model\n",
        "model = tf.keras.Sequential([tf.keras.layers.Dense(10,input_shape=[1],name=\"input_layer\"),\n",
        "                             tf.keras.layers.Dense(1,name=\"Output_layer\")],\n",
        "                            name = \"model_1\")\n",
        "# 2.Compile the model\n",
        "model.compile(loss=tf.keras.losses.mae,\n",
        "              optimizer=tf.keras.optimizers.SGD(),\n",
        "              metrics=[\"mae\"])\n",
        "# 3.Fit the model\n",
        "model.summary()\n",
        "#model.fit(tf.expand_dims(X, axis=-1), y, epochs=100)\n",
        "\n",
        "#dense is also called fully connected layer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yly2ujQQDDme",
        "outputId": "1e5fcca0-0039-40fb-ab12-a85c96efc42d"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\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": [
        "model.fit(X_train,y_train,epochs=100,verbose=0)"
      ],
      "metadata": {
        "id": "JadwC7REPIf5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc9546bc-049f-4c73-e6ff-eae6a9fb6304"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b166f08e920>"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model = model,\n",
        "           show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "MxmxNCN3s9GZ",
        "outputId": "38f202ed-1d1a-4c69-8cef-c92f7af818c1"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualizing our model's predictions\n",
        "Now we've got a trained model, let's visualize some predictions.\n",
        "\n",
        "To visualize predictions, it's always a good idea to plot them against the ground truth labels.\n",
        "\n",
        "Often you'll see this in the form of `y_test` vs. `y_pred` (ground truth vs. predictions).\n",
        "\n",
        "First, we'll make some predictions on the test data (X_test), remember the model has never seen the test data."
      ],
      "metadata": {
        "id": "Fz8Ms8ql4T2K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Km34BicY4Q9-",
        "outputId": "2daf38af-7be2-474d-ff22-abeb0bdad51d"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 61ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 70.398155],\n",
              "       [ 74.97758 ],\n",
              "       [ 79.55701 ],\n",
              "       [ 84.13643 ],\n",
              "       [ 88.71585 ],\n",
              "       [ 93.29528 ],\n",
              "       [ 97.8747  ],\n",
              "       [102.45412 ],\n",
              "       [107.03354 ],\n",
              "       [111.61297 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_4e9VF2eF0ZU",
        "outputId": "358b57a2-9adf-40c0-8e0e-9d88cdb0e946"
      },
      "execution_count": 157,
      "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": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's create a plotting function\n",
        "\n",
        "# Function is used becuse we can reuse this in future\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",
        "  plt.figure(figsize=(10,7))\n",
        "  # Plot training data in blue\n",
        "  plt.scatter(train_data,train_labels,c=\"b\",label = \"Training Data\")\n",
        "  # Plot testing data in green\n",
        "  plt.scatter(test_data,test_labels,c=\"g\",label = \"Testing Data\")\n",
        "  # Plot model's predicitions in red\n",
        "  plt.scatter(test_data,predictions,c=\"r\",label = \"Predictions\")\n",
        "  # Show the legend\n",
        "  plt.legend();"
      ],
      "metadata": {
        "id": "xqT2_iDcF3CE"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "jMW8LWqpIhTT",
        "outputId": "b237eb03-b577-4de4-e2e2-d4122361c605"
      },
      "execution_count": 159,
      "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 our model's predictions with regression evaluation metrics\n",
        "Alongisde visualizations, evaulation metrics are your alternative best option for evaluating your model.\n",
        "\n",
        "Depending on the problem you're working on, different models have different evaluation metrics.\n",
        "\n",
        "Two of the main metrics used for regression problems are:\n",
        "\n",
        "`* Mean absolute error (MAE)` - the mean\n",
        "difference between each of the predictions. (\"`on avg, how wrong is each of my model's predictions`\")\n",
        "\n",
        "`* Mean squared error (MSE)` - the squared mean difference between of the predictions (use if larger errors are more detrimental than smaller errors).\n",
        "The lower each of these values, the better. (\"`square the avg errors`\")\n",
        "\n",
        "You can also use `model.evaluate()` which will return the loss of the model as well as any metrics setup during the compile step."
      ],
      "metadata": {
        "id": "Tab6g3X4JDse"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test\n",
        "model.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWharWjIMGPM",
        "outputId": "3eb96cf4-89b4-4582-9c30-7a349718d045"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 115ms/step - loss: 3.0056 - mae: 3.0056\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3.0055618286132812, 3.0055618286132812]"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#MAE\n",
        "mae = tf.metrics.mean_absolute_error(y_true = y_test,\n",
        "                                     y_pred = tf.squeeze(y_pred))\n",
        "mae\n",
        "# this value and model.evaluate value is same"
      ],
      "metadata": {
        "id": "ARR6ZBl_JPzb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26470b43-0fca-4f6e-981a-75da37a36d7d"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=3.0055625>"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.squeeze(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvtJWuWxH7ou",
        "outputId": "74adc268-0e52-4e6d-b1e7-d1bb289f1226"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([ 70.398155,  74.97758 ,  79.55701 ,  84.13643 ,  88.71585 ,\n",
              "        93.29528 ,  97.8747  , 102.45412 , 107.03354 , 111.61297 ],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.metrics.mean_squared_error(y_true = y_test,\n",
        "                              y_pred = tf.squeeze(y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yfi_qQHXIEj1",
        "outputId": "25e72746-4bc9-4fae-d36b-5fac8d96e0e6"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=11.80319>"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mae(y_true,y_pred):\n",
        "  return tf.metrics.mean_absolute_error(y_true = y_test,\n",
        "                                     y_pred = tf.squeeze(y_pred))\n",
        "def mse(y_true,y_pred):\n",
        "  return tf.metrics.mean_absolute_error(y_true = y_test,\n",
        "                                     y_pred = tf.squeeze(y_pred))"
      ],
      "metadata": {
        "id": "bjEy8uINIk6I"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Running experiments to improve a model\n",
        "After seeing the evaluation metrics and the predictions your model makes, it's likely you'll want to improve it.\n",
        "\n",
        "Again, there are many different ways you can do this, but 3 of the main ones are:\n",
        "\n",
        "`Get more data` - get more examples for your model to train on (more opportunities to learn patterns).\n",
        "\n",
        "`Make your model larger (use a more complex model)` - this might come in the form of more layers or more hidden units in each layer.\n",
        "\n",
        "`Train for longer` - give your model more of a chance to find the patterns in the data.\n",
        "\n",
        "Since we created our dataset, we could easily make more data but this isn't always the case when you're working with real-world datasets.\n",
        "\n",
        "So let's take a look at how we can improve our model using 2 and 3.\n",
        "\n",
        "To do so, we'll build 3 models and compare their results:\n",
        "\n",
        "`model_1` - same as original model, 1 layer, trained for 50 epochs.\n",
        "\n",
        "`model_2` - 2 layers, trained for 100 epochs.\n",
        "\n",
        "`model_3` - 2 layers, trained for 500 epochs.\n",
        "\n",
        "# Build `model_1`"
      ],
      "metadata": {
        "id": "Q6NGZKfsLIN0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "#1. Create model\n",
        "model_1 = tf.keras.Sequential([tf.keras.layers.Dense(1)])\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=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHMk71chKIPj",
        "outputId": "d46ceea8-84c2-4b50-d5c0-b7e84cef3e07"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 12.0340 - mae: 12.0340\n",
            "Epoch 2/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.1726 - mae: 13.1726\n",
            "Epoch 3/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.8387 - mae: 11.8387\n",
            "Epoch 4/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.2406 - mae: 9.2406\n",
            "Epoch 5/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.1015 - mae: 10.1015\n",
            "Epoch 6/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.3711 - mae: 9.3711\n",
            "Epoch 7/50\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.5201 - mae: 8.5201\n",
            "Epoch 8/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.0341 - mae: 9.0341\n",
            "Epoch 9/50\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 18.6372 - mae: 18.6372\n",
            "Epoch 10/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.0462 - mae: 10.0462\n",
            "Epoch 11/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.3678 - mae: 8.3678\n",
            "Epoch 12/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.6082 - mae: 10.6082\n",
            "Epoch 13/50\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 9.7717 - mae: 9.7717\n",
            "Epoch 14/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 15.8892 - mae: 15.8892\n",
            "Epoch 15/50\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 11.4942 - mae: 11.4942\n",
            "Epoch 16/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.5061 - mae: 8.5061\n",
            "Epoch 17/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.5647 - mae: 13.5647\n",
            "Epoch 18/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.3769 - mae: 11.3769\n",
            "Epoch 19/50\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 18.0065 - mae: 18.0065\n",
            "Epoch 20/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 15.1404 - mae: 15.1404\n",
            "Epoch 21/50\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 11.0958 - mae: 11.0958\n",
            "Epoch 22/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.2222 - mae: 8.2222\n",
            "Epoch 23/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.4546 - mae: 9.4546\n",
            "Epoch 24/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.6724 - mae: 7.6724\n",
            "Epoch 25/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.0599 - mae: 13.0599\n",
            "Epoch 26/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.5107 - mae: 16.5107\n",
            "Epoch 27/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.2392 - mae: 13.2392\n",
            "Epoch 28/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.3507 - mae: 14.3507\n",
            "Epoch 29/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.0892 - mae: 10.0892\n",
            "Epoch 30/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.4854 - mae: 16.4854\n",
            "Epoch 31/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 23.5084 - mae: 23.5084\n",
            "Epoch 32/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 7.3605 - mae: 7.3605\n",
            "Epoch 33/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.8038 - mae: 9.8038\n",
            "Epoch 34/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.2650 - mae: 12.2650\n",
            "Epoch 35/50\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.1383 - mae: 11.1383\n",
            "Epoch 36/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.3529 - mae: 13.3529\n",
            "Epoch 37/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.4540 - mae: 9.4540\n",
            "Epoch 38/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.1092 - mae: 10.1092\n",
            "Epoch 39/50\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 10.1902 - mae: 10.1902\n",
            "Epoch 40/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.9322 - mae: 10.9322\n",
            "Epoch 41/50\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 7.9184 - mae: 7.9184\n",
            "Epoch 42/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.0659 - mae: 10.0659\n",
            "Epoch 43/50\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.6852 - mae: 8.6852\n",
            "Epoch 44/50\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 12.1662 - mae: 12.1662\n",
            "Epoch 45/50\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.8218 - mae: 13.8218\n",
            "Epoch 46/50\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 8.4818 - mae: 8.4818\n",
            "Epoch 47/50\n",
            "2/2 [==============================] - 0s 42ms/step - loss: 9.1104 - mae: 9.1104\n",
            "Epoch 48/50\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 10.5888 - mae: 10.5888\n",
            "Epoch 49/50\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 7.7298 - mae: 7.7298\n",
            "Epoch 50/50\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 9.5128 - mae: 9.5128\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b166fb03100>"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "squrjUk1RBVo",
        "outputId": "2d6d7a2a-b2da-426f-b85e-d2c2d6a94b15"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([60, 64, 68, 72, 76, 80, 84, 88, 92, 96], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds_1=model_1.predict(X_test)\n",
        "plot_predictions(predictions=y_preds_1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "2Clz9eD0NHwh",
        "outputId": "425f0ceb-67f2-416b-dae9-b7c48045e943"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 166ms/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": [
        "mae_1 = mae(y_test, tf.squeeze(y_preds_1)).numpy()\n",
        "mse_1 = mse(y_test, tf.squeeze(y_preds_1)).numpy()\n",
        "mae_1, mse_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDnIpeO0QjLj",
        "outputId": "3c3b47ac-579a-4685-85cc-1960f4ee69f0"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13.028119, 13.028119)"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Build `model_2`"
      ],
      "metadata": {
        "id": "aDeiDF3lR0or"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "model_2 = tf.keras.Sequential([tf.keras.layers.Dense(10),\n",
        "                               tf.keras.layers.Dense(1)])\n",
        "\n",
        "#2. Compile the model\n",
        "model_2.compile(loss=tf.keras.losses.mae,\n",
        "                optimizer=tf.keras.optimizers.SGD(),\n",
        "                metrics=[\"mse\"])\n",
        "\n",
        "#3. Fit 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": "XVMAtFPtR3_R",
        "outputId": "b2f4f48e-716d-411f-e24e-f71d38ab2b06"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 42.2021 - mse: 2649.0168\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 24.7439 - mse: 803.9892\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 29.1147 - mse: 1284.7025\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 13.4960 - mse: 247.2021\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 18.1700 - mse: 446.7079\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.5742 - mse: 104.3648\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.4023 - mse: 200.7434\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 10.9827 - mse: 142.9052\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 39.0930 - mse: 2381.9487\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 26.6681 - mse: 999.7493\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.7882 - mse: 218.9808\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 23.5681 - mse: 787.6206\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 21.2682 - mse: 645.7736\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 23.7549 - mse: 865.9944\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 15.8005 - mse: 340.1697\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 10.4409 - mse: 124.7197\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 19.7312 - mse: 530.6782\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.3535 - mse: 280.0145\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 16.3703 - mse: 430.0052\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.2388 - mse: 93.2068\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.4517 - mse: 289.3723\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.8718 - mse: 246.4805\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 15.4998 - mse: 315.3101\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 15.2907 - mse: 316.6411\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.3503 - mse: 272.7798\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 19.3720 - mse: 569.4936\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.4253 - mse: 167.5723\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 29.0087 - mse: 1367.2595\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.2394 - mse: 94.4935\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 29.7692 - mse: 1603.1030\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 54.0062 - mse: 5224.6646\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.5430 - mse: 100.7948\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.1331 - mse: 179.7291\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 23.8314 - mse: 860.1573\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.6236 - mse: 242.0272\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 21.5513 - mse: 662.5091\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.3483 - mse: 149.3927\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.4251 - mse: 267.4235\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.7647 - mse: 138.2812\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.5567 - mse: 397.8866\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.9447 - mse: 179.6730\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.2789 - mse: 115.0131\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.5796 - mse: 110.8651\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 27.9675 - mse: 1254.1198\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.2668 - mse: 146.5089\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.0504 - mse: 288.0887\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.5015 - mse: 256.5218\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.3376 - mse: 407.9520\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.5049 - mse: 98.3968\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 13.6958 - mse: 253.3503\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.5503 - mse: 152.8558\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 30.2410 - mse: 1577.0961\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.2176 - mse: 296.0460\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 23.5090 - mse: 831.1759\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 24.4167 - mse: 899.2589\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 11.2795 - mse: 171.3550\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 13.2807 - mse: 221.6772\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 9.9023 - mse: 108.3602\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.8230 - mse: 270.9857\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.9475 - mse: 118.3836\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.8842 - mse: 304.0158\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.8674 - mse: 193.4509\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.2528 - mse: 132.6158\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 23.7575 - mse: 829.0245\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.4289 - mse: 128.6090\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 20.8224 - mse: 636.9918\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.4019 - mse: 125.1328\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.1007 - mse: 299.2844\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 10.4687 - mse: 124.7083\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.5360 - mse: 199.3092\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 12.9691 - mse: 223.8119\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 19.2738 - mse: 527.0437\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.1008 - mse: 193.7026\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 21.3610 - mse: 711.7723\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 9.4291 - mse: 151.8212\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 12.2488 - mse: 181.9363\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 16.2658 - mse: 407.2225\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.9933 - mse: 91.4269\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 23.5102 - mse: 827.1901\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 26.1945 - mse: 1054.8619\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 11.4028 - mse: 159.5376\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 12.4563 - mse: 226.7541\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 17.1256 - mse: 381.6329\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 6.5506 - mse: 63.5010\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 20.4035 - mse: 582.5123\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.1612 - mse: 116.5617\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 24.4751 - mse: 838.6879\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 19.1373 - mse: 519.7308\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.1395 - mse: 69.4152\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 18.1965 - mse: 490.7235\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 13.2432 - mse: 253.9314\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 14.7711 - mse: 307.9330\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.0705 - mse: 216.6806\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 16.6586 - mse: 400.1865\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 16.0210 - mse: 347.6781\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.5504 - mse: 194.4754\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 21.1866 - mse: 641.3509\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 10.4481 - mse: 138.9545\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 14.5548 - mse: 294.4939\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 17.7327 - mse: 466.5467\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b1685d3c220>"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds_2=model_2.predict(X_test)\n",
        "plot_predictions(predictions=y_preds_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "f4EGxqyTbOnz",
        "outputId": "7de0c86f-1c4c-4f3e-b190-f8e8db55f842"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 49ms/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": [
        "mae_2 = mae(y_test, tf.squeeze(y_preds_2)).numpy()\n",
        "mse_2 = mse(y_test, tf.squeeze(y_preds_2)).numpy()\n",
        "mae_2, mse_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OK3L-idWcgs4",
        "outputId": "9c345f52-ab58-4b0a-9e9d-89ebd5b588c2"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.0802162, 2.0802162)"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Build `model_3`"
      ],
      "metadata": {
        "id": "-zz0s0gCc5iy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "#1.\n",
        "model_3 = tf.keras.Sequential([tf.keras.layers.Dense(10),\n",
        "                               tf.keras.layers.Dense(1)])\n",
        "#2.\n",
        "model_3.compile(loss = tf.keras.losses.mae,\n",
        "                optimizer=tf.keras.optimizers.SGD(),\n",
        "                metrics=[\"mae\"])\n",
        "#3.\n",
        "model_3.fit(tf.expand_dims(X_train,axis=-1),y_train,epochs=500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESQwXDDCcrOQ",
        "outputId": "8dfc9f19-6c8d-4067-b3fe-ce45144185b0"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 42.1704 - mae: 42.1704\n",
            "Epoch 2/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 10.9730 - mae: 10.9730\n",
            "Epoch 3/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 27.1096 - mae: 27.1096\n",
            "Epoch 4/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 13.7355 - mae: 13.7355\n",
            "Epoch 5/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 15.2658 - mae: 15.2658\n",
            "Epoch 6/500\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 12.1290 - mae: 12.1290\n",
            "Epoch 7/500\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 11.4446 - mae: 11.4446\n",
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            "Epoch 437/500\n",
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            "Epoch 439/500\n",
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            "2/2 [==============================] - 0s 7ms/step - loss: 9.2571 - mae: 9.2571\n",
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            "Epoch 493/500\n",
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            "Epoch 495/500\n",
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            "Epoch 498/500\n",
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            "Epoch 499/500\n",
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            "Epoch 500/500\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 14.6321 - mae: 14.6321\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b1685c60160>"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "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": 617
        },
        "id": "x1R8zxSFdrvE",
        "outputId": "720b0407-ab89-4435-8acf-42cc12d6be26"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 49ms/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": [
        "mae_3=mae(y_test,y_preds_3)\n",
        "mse_3=mse(y_test,y_preds_3)\n",
        "mae_3,mse_3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6NA0RJmlP8N",
        "outputId": "4d8d35a0-bab4-4554-f406-0408e9acfe22"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(), dtype=float32, numpy=68.69987>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=68.69987>)"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Comparing results of our models"
      ],
      "metadata": {
        "id": "Fg1figNdlm0U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's compare out model's results using pandas Dataframe\n",
        "import pandas as pd\n",
        "mr = [\n",
        "    [\"Model 1\", np.array(mae_1), np.array(mse_1)],\n",
        "    [\"Model 2\", np.array(mae_2), np.array(mse_2)],\n",
        "    [\"Model 3\", np.array(mae_3), np.array(mse_3)]\n",
        "]\n",
        "\n",
        "all_results = pd.DataFrame(mr, columns=[\"Models\", \"Mae\", \"Mse\"])\n",
        "all_results\n",
        "# lower the better. model 1 is better"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "aelikv3plmW6",
        "outputId": "4b54d9f5-c258-4ea7-84b4-085583b31ef0"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Models        Mae        Mse\n",
              "0  Model 1  13.028119  13.028119\n",
              "1  Model 2  2.0802162  2.0802162\n",
              "2  Model 3   68.69987   68.69987"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-71ba145a-dd97-4118-880a-53f95bcaddc2\" 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>Models</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>13.028119</td>\n",
              "      <td>13.028119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Model 2</td>\n",
              "      <td>2.0802162</td>\n",
              "      <td>2.0802162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Model 3</td>\n",
              "      <td>68.69987</td>\n",
              "      <td>68.69987</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-71ba145a-dd97-4118-880a-53f95bcaddc2')\"\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-71ba145a-dd97-4118-880a-53f95bcaddc2 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-71ba145a-dd97-4118-880a-53f95bcaddc2');\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-abb2d723-4ad2-4f2a-8e5f-a6aafa1adb0a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-abb2d723-4ad2-4f2a-8e5f-a6aafa1adb0a')\"\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-abb2d723-4ad2-4f2a-8e5f-a6aafa1adb0a 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": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tracking your experiments\n",
        "One really good habit to get into is tracking your modelling experiments to see which perform better than others.\n",
        "\n",
        "We've done a simple version of this above (keeping the results in different variables).\n",
        "\n",
        "📖 `Resource`: But as you build more models, you'll want to look into using tools such as:\n",
        "\n",
        "`TensorBoard` - a component of the TensorFlow library to help track modelling experiments (we'll see this later).\n",
        "\n",
        "`Weights & Biases` - a tool for tracking all kinds of machine learning experiments (the good news for Weights & Biases is it plugs into TensorBoard)."
      ],
      "metadata": {
        "id": "qofZ9U60uJdI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving a model\n",
        "Once you've trained a model and found one which performs to your liking, you'll probably want to save it for use elsewhere (like a web application or mobile device).\n",
        "\n",
        "You can save a TensorFlow/Keras model using model.save().\n",
        "\n",
        "There are two ways to save a model in TensorFlow:\n",
        "\n",
        "1. `The SavedModel format` (default).\n",
        "2. `The HDF5 format`.\n",
        "\n",
        "The main difference between the two is the SavedModel is automatically able to save custom objects (such as special layers) without additional modifications when loading the model back in.\n",
        "\n",
        "Which one should you use?\n",
        "\n",
        "It depends on your situation but the SavedModel format will suffice most of the time.\n",
        "\n",
        "Both methods use the same method call."
      ],
      "metadata": {
        "id": "lxC-_UOOuXWC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save a model using SavedModel format\n",
        "model_1.save(\"Best_model_SavedModel_format\")\n",
        "# get saved as folder"
      ],
      "metadata": {
        "id": "maOYgoFYt1a_"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save model using the HDF5 format\n",
        "model_1.save(\"Best_Model_HDF5_format.h5\")\n",
        "# get saved as file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbM3xmYDLvpv",
        "outputId": "3974cfdb-1f42-4c35-b2db-c8911e48eb9e"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3000: 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": "GaSEq3IGW6AO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # SavedModel format\n",
        "loaded_sm = tf.keras.models.load_model(\"Best_model_SavedModel_format\")\n",
        "loaded_sm.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NgWduBiEQ5AJ",
        "outputId": "25f4d410-ed55-4b22-ff16-33afecaf82c6"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_23\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_31 (Dense)            (None, 1)                 2         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2 (8.00 Byte)\n",
            "Trainable params: 2 (8.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.summary()\n",
        "# both are same"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g77Qxw0TXTiy",
        "outputId": "3674cfe7-beae-4a0a-d8fd-5fda2a8a53aa"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_23\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_31 (Dense)            (None, 1)                 2         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2 (8.00 Byte)\n",
            "Trainable params: 2 (8.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compare model_1 predictions with SavedModel format model perdictions\n",
        "model_1_preds = model_1.predict(X_test)\n",
        "loaded_sm_pred = loaded_sm.predict(X_test)\n",
        "model_1_preds == loaded_sm_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scCcdUY_XWBR",
        "outputId": "ff8ba4a5-c030-4bd6-c6a9-d06ebcd5b9cb"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 31ms/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": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load in a model using .h5 format\n",
        "loaded_h5 = tf.keras.models.load_model(\"/content/Best_Model_HDF5_format.h5\")\n",
        "loaded_h5.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7q-u3DTxX4og",
        "outputId": "00c841fc-def5-4d76-b5ab-95c94ce26db0"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_23\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_31 (Dense)            (None, 1)                 2         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2 (8.00 Byte)\n",
            "Trainable params: 2 (8.00 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 1\n",
        "model_2_preds = model_2.predict(X_test)\n",
        "loaded_h5_preds = loaded_h5.predict(X_test)\n",
        "model_2_preds == loaded_h5_preds\n",
        "# should get true but its false dk why"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHuFTEDKYnW9",
        "outputId": "0b99937b-c4a4-4d17-a178-a51f3fca5229"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[False],\n",
              "       [False],\n",
              "       [False],\n",
              "       [False],\n",
              "       [False],\n",
              "       [False],\n",
              "       [False],\n",
              "       [False],\n",
              "       [False],\n",
              "       [False]])"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download a model (or any other file) from Google colab\n",
        "Say you wanted to get your model from Google Colab to your local machine, you can do one of the following things:\n",
        "\n",
        "1. Right click on the file in the files pane and right click file, go to 'download'.\n",
        "2. Use the code below (first cell below)\n",
        "3. Save it to Google Drive and copying it there (second cell below)"
      ],
      "metadata": {
        "id": "UyWbQSywZq8_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download a file from Google colab\n",
        "from google.colab import files\n",
        "files.download(\"/content/Best_Model_HDF5_format.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "YS-4HA4NZmU7",
        "outputId": "2dc583a9-36e8-405a-e16b-c73d58405c12"
      },
      "execution_count": 183,
      "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_f13ee968-ba98-468a-b986-a2186441967f\", \"Best_Model_HDF5_format.h5\", 17600)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/Best_Model_HDF5_format.h5 /content/drive/MyDrive/Colab Notebooks/02_neural_network_with_tf.ipynb"
      ],
      "metadata": {
        "id": "wbzgSWADaw0q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c87dd0df-3079-4c70-a434-a82b84e4d6e5"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp: target 'Notebooks/02_neural_network_with_tf.ipynb' is not a directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ls /content/drive/MyDrive/Colab Notebooks/02_neural_network_with_tf.ipynb"
      ],
      "metadata": {
        "id": "s_frZD5Sap88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6651ffc5-f9d3-43d4-b353-4a6ceb1e18bc"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ls: cannot access '/content/drive/MyDrive/Colab': No such file or directory\n",
            "ls: cannot access 'Notebooks/02_neural_network_with_tf.ipynb': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# A Larger Example"
      ],
      "metadata": {
        "id": "TbZ9kr7Xb6nh"
      }
    },
    {
      "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": "HtDWs2sGb3KV"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the insurance dataset (machine learning cost dataset - kaggle)\n",
        "insurance = pd.read_csv(\"insurance.csv\")\n",
        "insurance"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "g0X2mI60djfP",
        "outputId": "e7671076-c590-4d72-dacf-20dfc05dca8b"
      },
      "execution_count": 187,
      "outputs": [
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              "      age     sex     bmi  children smoker     region      charges\n",
              "0      19  female  27.900         0    yes  southwest  16884.92400\n",
              "1      18    male  33.770         1     no  southeast   1725.55230\n",
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              "4      32    male  28.880         0     no  northwest   3866.85520\n",
              "...   ...     ...     ...       ...    ...        ...          ...\n",
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              "1334   18  female  31.920         0     no  northeast   2205.98080\n",
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              "1336   21  female  25.800         0     no  southwest   2007.94500\n",
              "1337   61  female  29.070         0    yes  northwest  29141.36030\n",
              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we have to turn the non-numerical columns into numerical. always the first process in DL\n",
        "# one hot encoding - categorical variables to numerical variables\n",
        "\n",
        "insurance_onehot = pd.get_dummies(insurance)\n",
        "insurance_onehot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "LAFSF168v18I",
        "outputId": "d8705922-1d87-48b0-bc65-839137f398b5"
      },
      "execution_count": 188,
      "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",
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              "1334   18  31.920         0   2205.98080           1         0          1   \n",
              "1335   18  36.850         0   1629.83350           1         0          1   \n",
              "1336   21  25.800         0   2007.94500           1         0          1   \n",
              "1337   61  29.070         0  29141.36030           1         0          0   \n",
              "\n",
              "      smoker_yes  region_northeast  region_northwest  region_southeast  \\\n",
              "0              1                 0                 0                 0   \n",
              "1              0                 0                 0                 1   \n",
              "2              0                 0                 0                 1   \n",
              "3              0                 0                 1                 0   \n",
              "4              0                 0                 1                 0   \n",
              "...          ...               ...               ...               ...   \n",
              "1333           0                 0                 1                 0   \n",
              "1334           0                 1                 0                 0   \n",
              "1335           0                 0                 0                 1   \n",
              "1336           0                 0                 0                 0   \n",
              "1337           1                 0                 1                 0   \n",
              "\n",
              "      region_southwest  \n",
              "0                    1  \n",
              "1                    0  \n",
              "2                    0  \n",
              "3                    0  \n",
              "4                    0  \n",
              "...                ...  \n",
              "1333                 0  \n",
              "1334                 0  \n",
              "1335                 0  \n",
              "1336                 1  \n",
              "1337                 0  \n",
              "\n",
              "[1338 rows x 12 columns]"
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>4449.46200</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>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",
              "      <td>0</td>\n",
              "      <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",
              "    <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",
              "      <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>30.970</td>\n",
              "      <td>3</td>\n",
              "      <td>10600.54830</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>2205.98080</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>1629.83350</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>21</td>\n",
              "      <td>25.800</td>\n",
              "      <td>0</td>\n",
              "      <td>2007.94500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>29.070</td>\n",
              "      <td>0</td>\n",
              "      <td>29141.36030</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17bae431-7b0e-4665-9324-c8bd950d1e72')\"\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",
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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",
              "      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-17bae431-7b0e-4665-9324-c8bd950d1e72 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-17bae431-7b0e-4665-9324-c8bd950d1e72');\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-f1cb1fe9-2b0c-48b7-8c74-d37d11cb3d81\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f1cb1fe9-2b0c-48b7-8c74-d37d11cb3d81')\"\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-f1cb1fe9-2b0c-48b7-8c74-d37d11cb3d81 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": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creae X & y values (features and labels)\n",
        "# every column except charges\n",
        "X = insurance_onehot.drop(\"charges\",axis=1)\n",
        "y = insurance_onehot[\"charges\"]"
      ],
      "metadata": {
        "id": "zjaDFjTVyrDU"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# view X\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "H2NGZgaEz_aL",
        "outputId": "5a9225de-cce5-49a2-cec8-866e62fd261c"
      },
      "execution_count": 191,
      "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": [
              "\n",
              "  <div id=\"df-7961a21d-1199-4e5a-b178-6bd09961e0a6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\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",
              "      <th>region_northwest</th>\n",
              "      <th>region_southeast</th>\n",
              "      <th>region_southwest</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</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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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
              "  <style>\n",
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              "\n",
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              "      height: 32px;\n",
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              "\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",
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              "    }\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-7961a21d-1199-4e5a-b178-6bd09961e0a6 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-7961a21d-1199-4e5a-b178-6bd09961e0a6');\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-590e2f2e-8282-4bd1-b3ac-da678ac3a77d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-590e2f2e-8282-4bd1-b3ac-da678ac3a77d')\"\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-590e2f2e-8282-4bd1-b3ac-da678ac3a77d 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": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhaChPwi0b_y",
        "outputId": "1b1ebbd9-da06-4714-c452-fff47e69ef99"
      },
      "execution_count": 192,
      "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": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create training and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
        "len(X), len(X_train), len(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJe3-f0JzsIV",
        "outputId": "f2d63992-2c5d-4f35-ab14-c0b2d2eb2898"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1338, 1070, 268)"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#`MODEL - 1`"
      ],
      "metadata": {
        "id": "wzU0dEhwEeUU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a neural network (sort of like model 1 above)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create a model\n",
        "insurance_model = tf.keras.Sequential([tf.keras.layers.Dense(10),\n",
        "                                       tf.keras.layers.Dense(1)])\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": "C0VNIccHzu1K",
        "outputId": "e5808f7a-eefe-4970-e3f5-66c1d064098b"
      },
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8833.1084 - mae: 8833.1084\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7871.2925 - mae: 7871.2925\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7501.0840 - mae: 7501.0840\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7787.3242 - mae: 7787.3242\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7688.7466 - mae: 7688.7466\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7645.5000 - mae: 7645.5000\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7545.2510 - mae: 7545.2510\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7735.7837 - mae: 7735.7837\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7554.0786 - mae: 7554.0786\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7694.0874 - mae: 7694.0874\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7518.3545 - mae: 7518.3545\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7468.9561 - mae: 7468.9561\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7681.5381 - mae: 7681.5381\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7819.8022 - mae: 7819.8022\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7535.1680 - mae: 7535.1680\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7690.2158 - mae: 7690.2158\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7566.1733 - mae: 7566.1733\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7715.5420 - mae: 7715.5420\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7802.4663 - mae: 7802.4663\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7884.4868 - mae: 7884.4868\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7508.3999 - mae: 7508.3999\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7753.3364 - mae: 7753.3364\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7671.0215 - mae: 7671.0215\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7395.3013 - mae: 7395.3013\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7595.7095 - mae: 7595.7095\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7670.5796 - mae: 7670.5796\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7705.0591 - mae: 7705.0591\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7574.0586 - mae: 7574.0586\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7681.6719 - mae: 7681.6719\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7657.3706 - mae: 7657.3706\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7691.5024 - mae: 7691.5024\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7469.9707 - mae: 7469.9707\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7484.8379 - mae: 7484.8379\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7506.4692 - mae: 7506.4692\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7608.7354 - mae: 7608.7354\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7595.2534 - mae: 7595.2534\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7370.8940 - mae: 7370.8940\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7549.1011 - mae: 7549.1011\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7555.1836 - mae: 7555.1836\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7579.3799 - mae: 7579.3799\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7716.8486 - mae: 7716.8486\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7709.2983 - mae: 7709.2983\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7622.0527 - mae: 7622.0527\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7479.8613 - mae: 7479.8613\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7488.5913 - mae: 7488.5913\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7422.0010 - mae: 7422.0010\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7491.7129 - mae: 7491.7129\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7492.8945 - mae: 7492.8945\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7534.3989 - mae: 7534.3989\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7507.7993 - mae: 7507.7993\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7391.9619 - mae: 7391.9619\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7542.2085 - mae: 7542.2085\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7486.6851 - mae: 7486.6851\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7208.7139 - mae: 7208.7139\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7208.8062 - mae: 7208.8062\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7447.3945 - mae: 7447.3945\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7469.7100 - mae: 7469.7100\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7560.8687 - mae: 7560.8687\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7586.0464 - mae: 7586.0464\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7360.9946 - mae: 7360.9946\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7375.8887 - mae: 7375.8887\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7381.9346 - mae: 7381.9346\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7385.7134 - mae: 7385.7134\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7283.1680 - mae: 7283.1680\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7366.0562 - mae: 7366.0562\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7431.3184 - mae: 7431.3184\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7367.1904 - mae: 7367.1904\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7538.9800 - mae: 7538.9800\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7443.0483 - mae: 7443.0483\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7673.6377 - mae: 7673.6377\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7327.4058 - mae: 7327.4058\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7305.1851 - mae: 7305.1851\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7482.6782 - mae: 7482.6782\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7446.1465 - mae: 7446.1465\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7265.0493 - mae: 7265.0493\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7292.0342 - mae: 7292.0342\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7337.6494 - mae: 7337.6494\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7374.8237 - mae: 7374.8237\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7528.0322 - mae: 7528.0322\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7384.9077 - mae: 7384.9077\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7206.7461 - mae: 7206.7461\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7159.0527 - mae: 7159.0527\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7528.9092 - mae: 7528.9092\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7123.2241 - mae: 7123.2241\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7531.8916 - mae: 7531.8916\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7445.9434 - mae: 7445.9434\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6966.5063 - mae: 6966.5063\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7411.0977 - mae: 7411.0977\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7511.7593 - mae: 7511.7593\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7063.0356 - mae: 7063.0356\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7309.0566 - mae: 7309.0566\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7270.9155 - mae: 7270.9155\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7512.5562 - mae: 7512.5562\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7191.3809 - mae: 7191.3809\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7167.1353 - mae: 7167.1353\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7220.3975 - mae: 7220.3975\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7183.7212 - mae: 7183.7212\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7409.8623 - mae: 7409.8623\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7379.2739 - mae: 7379.2739\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7189.0630 - mae: 7189.0630\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b1685d3dba0>"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "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": "M17bgcvm9XgO",
        "outputId": "2b1eed2a-1394-46d3-b6a8-57e74a12867e"
      },
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 7054.6631 - mae: 7054.6631\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7054.6630859375, 7054.6630859375]"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.median(), y_train.mean()\n",
        "# comparing these values to mae that we got as op above says that the model is bad."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NtyZn4P9n59",
        "outputId": "260c33ef-5cfd-4d0d-aa0f-887ce1260313"
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9575.4421, 13346.089736364485)"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#`MODEL - 2`\n",
        "The model isn't performing well, let's try and improve it\n",
        "To try to improve our model,\n",
        "1. add extra layer\n",
        "2. train for longer\n"
      ],
      "metadata": {
        "id": "ZwYNCNZZ_3Aq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Create a model\n",
        "insurance_model_2 = tf.keras.Sequential([tf.keras.layers.Dense(100),\n",
        "                                        tf.keras.layers.Dense(10),\n",
        "                                        tf.keras.layers.Dense(1)])\n",
        "#one layer added\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",
        "# SGD changed to Adam()\n",
        "\n",
        "# 3. Fit the model\n",
        "insurance_model_2.fit(X_train, y_train, epochs = 100, verbose = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-3Q5V9l_ysm",
        "outputId": "f686d1bc-2068-4f9d-ab52-21f30887fce6"
      },
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 1s 2ms/step - loss: 13316.8682 - mae: 13316.8682\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 13164.6768 - mae: 13164.6768\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12855.2285 - mae: 12855.2285\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 12249.1934 - mae: 12249.1934\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 11207.9893 - mae: 11207.9893\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 9790.3916 - mae: 9790.3916\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8386.6133 - mae: 8386.6133\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7592.0547 - mae: 7592.0547\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7431.0342 - mae: 7431.0342\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7408.4600 - mae: 7408.4600\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7389.6245 - mae: 7389.6245\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7368.3442 - mae: 7368.3442\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7348.4165 - mae: 7348.4165\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7326.4233 - mae: 7326.4233\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7307.9775 - mae: 7307.9775\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7286.1318 - mae: 7286.1318\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7266.3389 - mae: 7266.3389\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7243.3213 - mae: 7243.3213\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7221.2031 - mae: 7221.2031\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7197.8423 - mae: 7197.8423\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7179.8740 - mae: 7179.8740\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7151.4521 - mae: 7151.4521\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7126.3364 - mae: 7126.3364\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7101.4648 - mae: 7101.4648\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7083.7485 - mae: 7083.7485\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7050.4019 - mae: 7050.4019\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7022.1260 - mae: 7022.1260\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6993.5103 - mae: 6993.5103\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6964.5146 - mae: 6964.5146\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6936.2324 - mae: 6936.2324\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6904.2930 - mae: 6904.2930\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6875.4517 - mae: 6875.4517\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6842.8589 - mae: 6842.8589\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6810.0396 - mae: 6810.0396\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6774.4609 - mae: 6774.4609\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6739.3457 - mae: 6739.3457\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6701.6318 - mae: 6701.6318\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6670.2979 - mae: 6670.2979\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6631.7271 - mae: 6631.7271\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6596.9404 - mae: 6596.9404\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6566.1758 - mae: 6566.1758\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6541.6260 - mae: 6541.6260\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6514.7412 - mae: 6514.7412\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6493.9727 - mae: 6493.9727\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6483.6357 - mae: 6483.6357\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6466.4272 - mae: 6466.4272\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6449.8599 - mae: 6449.8599\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6435.5820 - mae: 6435.5820\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6423.5273 - mae: 6423.5273\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6409.7969 - mae: 6409.7969\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6396.6743 - mae: 6396.6743\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6384.7573 - mae: 6384.7573\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6370.4858 - mae: 6370.4858\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6356.9028 - mae: 6356.9028\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6343.2368 - mae: 6343.2368\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6329.2241 - mae: 6329.2241\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6316.8755 - mae: 6316.8755\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6302.2051 - mae: 6302.2051\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6287.5674 - mae: 6287.5674\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6276.9058 - mae: 6276.9058\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6257.2002 - mae: 6257.2002\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6244.7598 - mae: 6244.7598\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6226.5957 - mae: 6226.5957\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6209.7563 - mae: 6209.7563\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6192.7231 - mae: 6192.7231\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6175.5020 - mae: 6175.5020\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6163.1587 - mae: 6163.1587\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6141.0996 - mae: 6141.0996\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6124.7954 - mae: 6124.7954\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6104.7021 - mae: 6104.7021\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6085.0376 - mae: 6085.0376\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6065.9878 - mae: 6065.9878\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6052.5356 - mae: 6052.5356\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6024.0630 - mae: 6024.0630\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6002.6421 - mae: 6002.6421\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5989.3530 - mae: 5989.3530\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5957.0029 - mae: 5957.0029\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5934.2666 - mae: 5934.2666\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5910.3687 - mae: 5910.3687\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5882.9683 - mae: 5882.9683\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5857.4907 - mae: 5857.4907\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5830.2622 - mae: 5830.2622\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5802.4863 - mae: 5802.4863\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5770.1030 - mae: 5770.1030\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5743.3896 - mae: 5743.3896\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5710.9585 - mae: 5710.9585\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5675.2827 - mae: 5675.2827\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5640.8745 - mae: 5640.8745\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5604.0830 - mae: 5604.0830\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5563.8271 - mae: 5563.8271\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5529.9116 - mae: 5529.9116\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5481.9956 - mae: 5481.9956\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5441.1426 - mae: 5441.1426\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5397.2749 - mae: 5397.2749\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5346.7007 - mae: 5346.7007\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5304.6768 - mae: 5304.6768\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 5254.5615 - mae: 5254.5615\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5195.8315 - mae: 5195.8315\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5139.0078 - mae: 5139.0078\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5083.8345 - mae: 5083.8345\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b166e920c70>"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating\n",
        "insurance_model_2.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-nwnkOOCDIk",
        "outputId": "0c9abf14-3a7a-49d5-e35a-960eef09bd78"
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 3ms/step - loss: 4954.8184 - mae: 4954.8184\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4954.818359375, 4954.818359375]"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insurance_model.evaluate(X_test, y_test)\n",
        "# see the mae diff btwn the previous one and now"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RL0BCYlJC0tr",
        "outputId": "76d988b2-62d3-4d06-feef-fb71109b5d39"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 7054.6631 - mae: 7054.6631\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7054.6630859375, 7054.6630859375]"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#`MODEL - 3`"
      ],
      "metadata": {
        "id": "GmT76xBmEpF8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Improving again\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1.\n",
        "insurance_model_3 = tf.keras.Sequential([tf.keras.layers.Dense(100),\n",
        "                                        tf.keras.layers.Dense(10),\n",
        "                                        tf.keras.layers.Dense(1)])\n",
        "# 2.\n",
        "insurance_model_3.compile(loss = tf.keras.losses.mae,\n",
        "                        optimizer = tf.keras.optimizers.Adam(),\n",
        "                        metrics = [\"mae\"])\n",
        "# 3.\n",
        "history = insurance_model_3.fit(X_train, y_train, epochs = 300, verbose =1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVvKaLlODEHX",
        "outputId": "8f0b3d94-9d8a-49b6-9126-573914e92bc1"
      },
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "34/34 [==============================] - 1s 2ms/step - loss: 13316.9141 - mae: 13316.9141\n",
            "Epoch 2/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 13204.3047 - mae: 13204.3047\n",
            "Epoch 3/300\n",
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            "Epoch 169/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3718.1760 - mae: 3718.1760\n",
            "Epoch 170/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3712.8154 - mae: 3712.8154\n",
            "Epoch 171/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3711.9832 - mae: 3711.9832\n",
            "Epoch 172/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3714.8782 - mae: 3714.8782\n",
            "Epoch 173/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3708.2419 - mae: 3708.2419\n",
            "Epoch 174/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3713.3669 - mae: 3713.3669\n",
            "Epoch 175/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3718.5676 - mae: 3718.5676\n",
            "Epoch 176/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3710.5266 - mae: 3710.5266\n",
            "Epoch 177/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3706.9175 - mae: 3706.9175\n",
            "Epoch 178/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3703.8328 - mae: 3703.8328\n",
            "Epoch 179/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3712.9810 - mae: 3712.9810\n",
            "Epoch 180/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3715.1414 - mae: 3715.1414\n",
            "Epoch 181/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3701.8191 - mae: 3701.8191\n",
            "Epoch 182/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3698.6497 - mae: 3698.6497\n",
            "Epoch 183/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3696.2417 - mae: 3696.2417\n",
            "Epoch 184/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3703.1782 - mae: 3703.1782\n",
            "Epoch 185/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3700.1152 - mae: 3700.1152\n",
            "Epoch 186/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3702.5881 - mae: 3702.5881\n",
            "Epoch 187/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3692.5271 - mae: 3692.5271\n",
            "Epoch 188/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3698.6602 - mae: 3698.6602\n",
            "Epoch 189/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3688.5117 - mae: 3688.5117\n",
            "Epoch 190/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3689.4868 - mae: 3689.4868\n",
            "Epoch 191/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3704.6887 - mae: 3704.6887\n",
            "Epoch 192/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3690.2419 - mae: 3690.2419\n",
            "Epoch 193/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3680.0930 - mae: 3680.0930\n",
            "Epoch 194/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3680.9878 - mae: 3680.9878\n",
            "Epoch 195/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3678.7898 - mae: 3678.7898\n",
            "Epoch 196/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3685.8765 - mae: 3685.8765\n",
            "Epoch 197/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3671.2092 - mae: 3671.2092\n",
            "Epoch 198/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3677.5676 - mae: 3677.5676\n",
            "Epoch 199/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3686.4138 - mae: 3686.4138\n",
            "Epoch 200/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3672.4211 - mae: 3672.4211\n",
            "Epoch 201/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3672.2097 - mae: 3672.2097\n",
            "Epoch 202/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3672.8425 - mae: 3672.8425\n",
            "Epoch 203/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3669.4895 - mae: 3669.4895\n",
            "Epoch 204/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3660.9067 - mae: 3660.9067\n",
            "Epoch 205/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3669.4624 - mae: 3669.4624\n",
            "Epoch 206/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3662.9231 - mae: 3662.9231\n",
            "Epoch 207/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3662.1423 - mae: 3662.1423\n",
            "Epoch 208/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3658.8857 - mae: 3658.8857\n",
            "Epoch 209/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3659.3557 - mae: 3659.3557\n",
            "Epoch 210/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3664.4375 - mae: 3664.4375\n",
            "Epoch 211/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3658.8296 - mae: 3658.8296\n",
            "Epoch 212/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3675.1917 - mae: 3675.1917\n",
            "Epoch 213/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3663.2061 - mae: 3663.2061\n",
            "Epoch 214/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3679.5750 - mae: 3679.5750\n",
            "Epoch 215/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3659.4338 - mae: 3659.4338\n",
            "Epoch 216/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3651.1128 - mae: 3651.1128\n",
            "Epoch 217/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3653.1580 - mae: 3653.1580\n",
            "Epoch 218/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3648.6721 - mae: 3648.6721\n",
            "Epoch 219/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3658.0613 - mae: 3658.0613\n",
            "Epoch 220/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3643.4536 - mae: 3643.4536\n",
            "Epoch 221/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3645.8486 - mae: 3645.8486\n",
            "Epoch 222/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3653.0359 - mae: 3653.0359\n",
            "Epoch 223/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3640.6985 - mae: 3640.6985\n",
            "Epoch 224/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3644.2373 - mae: 3644.2373\n",
            "Epoch 225/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3638.1975 - mae: 3638.1975\n",
            "Epoch 226/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3634.4883 - mae: 3634.4883\n",
            "Epoch 227/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3630.0864 - mae: 3630.0864\n",
            "Epoch 228/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3637.8293 - mae: 3637.8293\n",
            "Epoch 229/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3637.2986 - mae: 3637.2986\n",
            "Epoch 230/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3624.4841 - mae: 3624.4841\n",
            "Epoch 231/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3623.1318 - mae: 3623.1318\n",
            "Epoch 232/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3620.4219 - mae: 3620.4219\n",
            "Epoch 233/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3618.9333 - mae: 3618.9333\n",
            "Epoch 234/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3630.2498 - mae: 3630.2498\n",
            "Epoch 235/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3631.2280 - mae: 3631.2280\n",
            "Epoch 236/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3623.7649 - mae: 3623.7649\n",
            "Epoch 237/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3623.1355 - mae: 3623.1355\n",
            "Epoch 238/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3605.8521 - mae: 3605.8521\n",
            "Epoch 239/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3614.4934 - mae: 3614.4934\n",
            "Epoch 240/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3612.5735 - mae: 3612.5735\n",
            "Epoch 241/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3606.5583 - mae: 3606.5583\n",
            "Epoch 242/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3605.2737 - mae: 3605.2737\n",
            "Epoch 243/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3601.2737 - mae: 3601.2737\n",
            "Epoch 244/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3612.2109 - mae: 3612.2109\n",
            "Epoch 245/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3600.1655 - mae: 3600.1655\n",
            "Epoch 246/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3595.1106 - mae: 3595.1106\n",
            "Epoch 247/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3594.9270 - mae: 3594.9270\n",
            "Epoch 248/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3615.1418 - mae: 3615.1418\n",
            "Epoch 249/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3595.5569 - mae: 3595.5569\n",
            "Epoch 250/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3589.0190 - mae: 3589.0190\n",
            "Epoch 251/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3585.6560 - mae: 3585.6560\n",
            "Epoch 252/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3588.9070 - mae: 3588.9070\n",
            "Epoch 253/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3583.0007 - mae: 3583.0007\n",
            "Epoch 254/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3580.2021 - mae: 3580.2021\n",
            "Epoch 255/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3574.5398 - mae: 3574.5398\n",
            "Epoch 256/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3579.9072 - mae: 3579.9072\n",
            "Epoch 257/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3576.6208 - mae: 3576.6208\n",
            "Epoch 258/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3592.9011 - mae: 3592.9011\n",
            "Epoch 259/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3570.1555 - mae: 3570.1555\n",
            "Epoch 260/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3572.9314 - mae: 3572.9314\n",
            "Epoch 261/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3572.0452 - mae: 3572.0452\n",
            "Epoch 262/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3567.4070 - mae: 3567.4070\n",
            "Epoch 263/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3567.7468 - mae: 3567.7468\n",
            "Epoch 264/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3567.6177 - mae: 3567.6177\n",
            "Epoch 265/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3565.8677 - mae: 3565.8677\n",
            "Epoch 266/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3566.6355 - mae: 3566.6355\n",
            "Epoch 267/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3566.5967 - mae: 3566.5967\n",
            "Epoch 268/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3557.4070 - mae: 3557.4070\n",
            "Epoch 269/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3563.3367 - mae: 3563.3367\n",
            "Epoch 270/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3553.6079 - mae: 3553.6079\n",
            "Epoch 271/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3554.9543 - mae: 3554.9543\n",
            "Epoch 272/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3551.2249 - mae: 3551.2249\n",
            "Epoch 273/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3550.9995 - mae: 3550.9995\n",
            "Epoch 274/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3549.3367 - mae: 3549.3367\n",
            "Epoch 275/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3545.7117 - mae: 3545.7117\n",
            "Epoch 276/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3549.2146 - mae: 3549.2146\n",
            "Epoch 277/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3546.1794 - mae: 3546.1794\n",
            "Epoch 278/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3549.2188 - mae: 3549.2188\n",
            "Epoch 279/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3546.0742 - mae: 3546.0742\n",
            "Epoch 280/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3540.0759 - mae: 3540.0759\n",
            "Epoch 281/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3546.5796 - mae: 3546.5796\n",
            "Epoch 282/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3537.2903 - mae: 3537.2903\n",
            "Epoch 283/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3541.5845 - mae: 3541.5845\n",
            "Epoch 284/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3536.9934 - mae: 3536.9934\n",
            "Epoch 285/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3539.8857 - mae: 3539.8857\n",
            "Epoch 286/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3529.0747 - mae: 3529.0747\n",
            "Epoch 287/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3529.8625 - mae: 3529.8625\n",
            "Epoch 288/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3528.8767 - mae: 3528.8767\n",
            "Epoch 289/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3529.9141 - mae: 3529.9141\n",
            "Epoch 290/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3534.3035 - mae: 3534.3035\n",
            "Epoch 291/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3534.1711 - mae: 3534.1711\n",
            "Epoch 292/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3534.7007 - mae: 3534.7007\n",
            "Epoch 293/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3523.9646 - mae: 3523.9646\n",
            "Epoch 294/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3532.3335 - mae: 3532.3335\n",
            "Epoch 295/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3538.5056 - mae: 3538.5056\n",
            "Epoch 296/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3523.6704 - mae: 3523.6704\n",
            "Epoch 297/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3514.7048 - mae: 3514.7048\n",
            "Epoch 298/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3519.8418 - mae: 3519.8418\n",
            "Epoch 299/300\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3530.1641 - mae: 3530.1641\n",
            "Epoch 300/300\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3517.9204 - mae: 3517.9204\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate\n",
        "insurance_model_3.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rKnVfj1VEQQI",
        "outputId": "e14fdfe1-ab69-452d-e2e7-01a7d0dca3bb"
      },
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 3231.3318 - mae: 3231.3318\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3231.331787109375, 3231.331787109375]"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insurance_model.evaluate(X_test, y_test)\n",
        "# see the mae diff btwn the previous one and now"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSru2ILJEamm",
        "outputId": "ffd59a81-c397-467d-a395-351ec28e00ed"
      },
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 7054.6631 - mae: 7054.6631\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7054.6630859375, 7054.6630859375]"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot history (also kown as a loss curve or a training curve)\n",
        "pd.DataFrame(history.history).plot()\n",
        "plt.ylabel(\"loss\")\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "Gl_mJYutEv-u",
        "outputId": "8a98e294-0b1d-46dd-fee5-2a9e6be8c462"
      },
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7b166d0599c0>"
            ]
          },
          "metadata": {},
          "execution_count": 227
        },
        {
          "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",
        "In terms of scaling values, neural networks tend to prefer normalization.\n"
      ],
      "metadata": {
        "id": "RKQ72AX0GJ-L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "rwz6fg-BFxwB",
        "outputId": "0cf20e1e-9eca-431d-b35c-ca037bd148fb"
      },
      "execution_count": 228,
      "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",
              "1333   50  30.970         3           0         1          1           0   \n",
              "1334   18  31.920         0           1         0          1           0   \n",
              "1335   18  36.850         0           1         0          1           0   \n",
              "1336   21  25.800         0           1         0          1           0   \n",
              "1337   61  29.070         0           1         0          0           1   \n",
              "\n",
              "      region_northeast  region_northwest  region_southeast  region_southwest  \n",
              "0                    0                 0                 0                 1  \n",
              "1                    0                 0                 1                 0  \n",
              "2                    0                 0                 1                 0  \n",
              "3                    0                 1                 0                 0  \n",
              "4                    0                 1                 0                 0  \n",
              "...                ...               ...               ...               ...  \n",
              "1333                 0                 1                 0                 0  \n",
              "1334                 1                 0                 0                 0  \n",
              "1335                 0                 0                 1                 0  \n",
              "1336                 0                 0                 0                 1  \n",
              "1337                 0                 1                 0                 0  \n",
              "\n",
              "[1338 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-851ba5ef-83f5-4ced-813d-73f9c300a833\" 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>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",
              "      <th>region_northwest</th>\n",
              "      <th>region_southeast</th>\n",
              "      <th>region_southwest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\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>30.970</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1334</th>\n",
              "      <td>18</td>\n",
              "      <td>31.920</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1335</th>\n",
              "      <td>18</td>\n",
              "      <td>36.850</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>21</td>\n",
              "      <td>25.800</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1337</th>\n",
              "      <td>61</td>\n",
              "      <td>29.070</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-851ba5ef-83f5-4ced-813d-73f9c300a833')\"\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-851ba5ef-83f5-4ced-813d-73f9c300a833 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-851ba5ef-83f5-4ced-813d-73f9c300a833');\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-8dfe7087-6218-415f-a08a-eb41d8c494a4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8dfe7087-6218-415f-a08a-eb41d8c494a4')\"\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-8dfe7087-6218-415f-a08a-eb41d8c494a4 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": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X[\"age\"].plot(kind=\"hist\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "cjSfUVp8G-tr",
        "outputId": "4f514c60-26b6-4342-9c79-81f1fddd6619"
      },
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 229
        },
        {
          "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[\"bmi\"].plot(kind=\"hist\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "i0CKJyPpHGyL",
        "outputId": "26b791c9-9ae1-4873-af5f-b8f221486f1f"
      },
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 230
        },
        {
          "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[\"children\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTs7y_jVHI3t",
        "outputId": "f8e633a1-f5d8-4939-ca0c-70fbb67ae5f0"
      },
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    574\n",
              "1    324\n",
              "2    240\n",
              "3    157\n",
              "4     25\n",
              "5     18\n",
              "Name: children, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "# Read the insurance\n",
        "insurance = pd.read_csv('insurance.csv')\n",
        "insurance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "vcsmXN2XHYDU",
        "outputId": "41f99ef2-ef98-4195-e128-5cbd1b8b845e"
      },
      "execution_count": 234,
      "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-83d13c70-fa81-4350-b400-434301efbb86\" 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-83d13c70-fa81-4350-b400-434301efbb86')\"\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .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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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-83d13c70-fa81-4350-b400-434301efbb86 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-83d13c70-fa81-4350-b400-434301efbb86');\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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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  </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",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      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-bcc73a53-0421-4b6b-8220-cf28f6d8c28a 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": 234
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To prepare our data, we can borrow a few classes from Scikit-Learn"
      ],
      "metadata": {
        "id": "SwMkWXy9ISMc"
      }
    },
    {
      "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\"]),  # turn all values in these columns between 0 and 1\n",
        "    (OneHotEncoder(handle_unknown=\"ignore\"), [\"sex\", \"smoker\", \"region\"])\n",
        ")\n",
        "\n",
        "# Create X and y\n",
        "X = insurance.drop(\"charges\", axis=1)\n",
        "y = insurance[\"charges\"]\n",
        "\n",
        "# Build our train and test split\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 our training data\n",
        "ct.fit(X_train)\n",
        "\n",
        "# Transform training and test data with normalization (MinMaxScaler) and OneHotEncoder\n",
        "X_train_norm = ct.transform(X_train)\n",
        "X_test_norm = ct.transform(X_test)\n"
      ],
      "metadata": {
        "id": "58nd-SSiIYPK"
      },
      "execution_count": 240,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# What does our data look like now\n",
        "X_train.loc[0] #originally looks like this"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0OObXAIVMuDw",
        "outputId": "c19164fa-4849-4a05-d2d6-2ea25dadb530"
      },
      "execution_count": 241,
      "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": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now like this\n",
        "X_train_norm[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QosHxWUvM16I",
        "outputId": "66ca6f1c-5446-455c-c350-d73f8ca28451"
      },
      "execution_count": 243,
      "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": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_train_norm.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFqdrCKMM9Q2",
        "outputId": "f36cc0d6-eba5-46ac-9977-c9646367c19d"
      },
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1070, 6), (1070, 11))"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our data has been normalized and one hot encoded. Now let's build neural network model on it and see how it works"
      ],
      "metadata": {
        "id": "XSvSg__mNJPc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a neural network model to fit on our normalized data\n",
        "tf.random.set_seed(42)\n",
        "# 1.\n",
        "insurance_model_4 = tf.keras.Sequential([tf.keras.layers.Dense(100),\n",
        "                                        tf.keras.layers.Dense(10),\n",
        "                                        tf.keras.layers.Dense(1)])\n",
        "# 2.\n",
        "insurance_model_4.compile(loss = tf.keras.losses.mae,\n",
        "                        optimizer = tf.keras.optimizers.Adam(),\n",
        "                        metrics = [\"mae\"])\n",
        "# 3.\n",
        "insurance_model_4.fit(X_train_norm, y_train, epochs = 100, verbose =1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtJqNDJ4NIR3",
        "outputId": "b5b0142b-2b4d-4b4f-a6f1-6f2b8d740321"
      },
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "34/34 [==============================] - 1s 2ms/step - loss: 13344.3467 - mae: 13344.3467\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 13337.1104 - mae: 13337.1104\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 13318.8838 - mae: 13318.8838\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 13279.1670 - mae: 13279.1670\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 13206.6768 - mae: 13206.6768\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 13090.2627 - mae: 13090.2627\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 12919.5342 - mae: 12919.5342\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 12684.2695 - mae: 12684.2695\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 12374.1455 - mae: 12374.1455\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 11983.0605 - mae: 11983.0605\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 11518.1465 - mae: 11518.1465\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 11011.0264 - mae: 11011.0264\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 10507.0107 - mae: 10507.0107\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 10006.0215 - mae: 10006.0215\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 9532.4766 - mae: 9532.4766\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 9108.3975 - mae: 9108.3975\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 8754.8984 - mae: 8754.8984\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 8468.9307 - mae: 8468.9307\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 8246.2500 - mae: 8246.2500\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 8094.0234 - mae: 8094.0234\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7980.5581 - mae: 7980.5581\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7903.9609 - mae: 7903.9609\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7843.8794 - mae: 7843.8794\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7790.4668 - mae: 7790.4668\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7751.3306 - mae: 7751.3306\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7699.5322 - mae: 7699.5322\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7657.4829 - mae: 7657.4829\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7615.2227 - mae: 7615.2227\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7572.9512 - mae: 7572.9512\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7529.7393 - mae: 7529.7393\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7486.0098 - mae: 7486.0098\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7442.1729 - mae: 7442.1729\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7398.2881 - mae: 7398.2881\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7350.4092 - mae: 7350.4092\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7303.9395 - mae: 7303.9395\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7254.3179 - mae: 7254.3179\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7204.5112 - mae: 7204.5112\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 7153.9580 - mae: 7153.9580\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7099.6807 - mae: 7099.6807\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 7044.9731 - mae: 7044.9731\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6989.1958 - mae: 6989.1958\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6930.4434 - mae: 6930.4434\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6868.7754 - mae: 6868.7754\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6803.1875 - mae: 6803.1875\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6736.7915 - mae: 6736.7915\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6668.6904 - mae: 6668.6904\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6598.2573 - mae: 6598.2573\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6520.5586 - mae: 6520.5586\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6442.5312 - mae: 6442.5312\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6358.0723 - mae: 6358.0723\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6274.7261 - mae: 6274.7261\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 6182.2036 - mae: 6182.2036\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 6087.2798 - mae: 6087.2798\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5989.8115 - mae: 5989.8115\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5883.9087 - mae: 5883.9087\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5776.8306 - mae: 5776.8306\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5662.3364 - mae: 5662.3364\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5545.0405 - mae: 5545.0405\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5428.3447 - mae: 5428.3447\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5306.3457 - mae: 5306.3457\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5179.9204 - mae: 5179.9204\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 5051.0239 - mae: 5051.0239\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 4922.4531 - mae: 4922.4531\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 4791.1006 - mae: 4791.1006\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 4662.9775 - mae: 4662.9775\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 4535.5020 - mae: 4535.5020\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 4424.5381 - mae: 4424.5381\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 4316.1450 - mae: 4316.1450\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 4213.4507 - mae: 4213.4507\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 4118.9189 - mae: 4118.9189\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4030.6223 - mae: 4030.6223\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3953.3079 - mae: 3953.3079\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3887.1582 - mae: 3887.1582\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3832.5789 - mae: 3832.5789\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3787.6489 - mae: 3787.6489\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3753.8223 - mae: 3753.8223\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3727.3540 - mae: 3727.3540\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3708.6831 - mae: 3708.6831\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3695.1755 - mae: 3695.1755\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3685.4402 - mae: 3685.4402\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3678.4753 - mae: 3678.4753\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3674.5642 - mae: 3674.5642\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3668.7009 - mae: 3668.7009\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3665.4880 - mae: 3665.4880\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3662.6191 - mae: 3662.6191\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3660.5911 - mae: 3660.5911\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3657.5999 - mae: 3657.5999\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3655.2942 - mae: 3655.2942\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3653.5940 - mae: 3653.5940\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3651.8906 - mae: 3651.8906\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3648.9082 - mae: 3648.9082\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3648.2256 - mae: 3648.2256\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 3646.0852 - mae: 3646.0852\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3644.3232 - mae: 3644.3232\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3645.4124 - mae: 3645.4124\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3642.2949 - mae: 3642.2949\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3639.8682 - mae: 3639.8682\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3638.0400 - mae: 3638.0400\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3637.0549 - mae: 3637.0549\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 1ms/step - loss: 3635.7957 - mae: 3635.7957\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b166d4f14e0>"
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate\n",
        "insurance_model_4.evaluate(X_test_norm,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4R4vYHqN2yJ",
        "outputId": "12e1e797-bb4f-41ff-91a7-d2101319e93a"
      },
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 3439.2104 - mae: 3439.2104\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3439.21044921875, 3439.21044921875]"
            ]
          },
          "metadata": {},
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compare this result with other model's results"
      ],
      "metadata": {
        "id": "-pGsMQgiOhRF"
      }
    }
  ]
}