01_Neural_network_regression_with_tensorflow_video.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "80736880",
   "metadata": {},
   "source": [
    "# Introduction to Regression with Neural Network in Tensorflow\n",
    "\n",
    "There are many definition for a regression problem but in our case, we are going to simplify it. \n",
    "Predicting a numerical variable based on some other combination of variables, even shorter...\n",
    "predicting a number.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34cd3fac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.10.0\n"
     ]
    }
   ],
   "source": [
    "# Import Tensorflow \n",
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1b8fdcd",
   "metadata": {},
   "source": [
    "## Create some data to view and fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b95eec14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14eb7ba4400>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create features\n",
    "\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",
    "\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b810cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y == X + 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8659d29e",
   "metadata": {},
   "source": [
    "## Input and output shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01a86a48",
   "metadata": {},
   "outputs": [
    {
     "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])>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a demo tensor for our housing prediction problem\n",
    "\n",
    "house_info = tf.constant([\"bedroom\",\"bathroom\",\"garage\"])\n",
    "house_price = tf.constant([939700])\n",
    "house_info, house_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "feb09558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8,), (8,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_shape = X.shape\n",
    "output_shape = y.shape\n",
    "input_shape, output_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c00a604f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-7.0, 3.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0], y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "643976f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(8,), dtype=float64, numpy=array([-7., -4., -1.,  2.,  5.,  8., 11., 14.])>,\n",
       " <tf.Tensor: shape=(8,), dtype=float64, numpy=array([ 3.,  6.,  9., 12., 15., 18., 21., 24.])>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Turn our NumPy arrays into tensors\n",
    "X = tf.constant(X)\n",
    "y = tf.constant(y)\n",
    "X,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "57f42c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([]), TensorShape([]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_shape = X[0].shape\n",
    "output_shape = y[0].shape\n",
    "input_shape, output_shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "378de0a8",
   "metadata": {},
   "source": [
    "### Steps in modelling with tensorflow\n",
    "\n",
    "1. Creating a Model - define the input and output layers, as well as hidden layers of a deep learning model.\n",
    "2. Compiling a model - Define the loss function, the function which tells our model how wrong it is and the optimizer\n",
    "    it tells us how to improve our model. And Evaluation Metrics \n",
    "3. Fitting a model - letting the model try to find patterns between X & y (feature and labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d346290e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1/1 [==============================] - 1s 784ms/step - loss: 10.3687 - mae: 10.3687\n",
      "Epoch 2/10\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 10.2362 - mae: 10.2362\n",
      "Epoch 3/10\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 10.1037 - mae: 10.1037\n",
      "Epoch 4/10\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 9.9712 - mae: 9.9712\n",
      "Epoch 5/10\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 9.8387 - mae: 9.8387\n",
      "Epoch 6/10\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 9.7062 - mae: 9.7062\n",
      "Epoch 7/10\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 9.5737 - mae: 9.5737\n",
      "Epoch 8/10\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 9.4412 - mae: 9.4412\n",
      "Epoch 9/10\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 9.3087 - mae: 9.3087\n",
      "Epoch 10/10\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 9.1762 - mae: 9.1762\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1f6ad3989a0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set a random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# 1. Create a model using the Sequential API\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "#2. Compile\n",
    "\n",
    "model.compile(loss=tf.keras.losses.mae,\n",
    "             optimizer=tf.keras.optimizers.SGD(),\n",
    "             metrics=[\"mae\"]) #Stochaistic gradient descent\n",
    "\n",
    "#3. Fit de Model\n",
    "model.fit(tf.expand_dims(X, axis=-1),y, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d6728873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(8,), dtype=float64, numpy=array([-7., -4., -1.,  2.,  5.,  8., 11., 14.])>,\n",
       " <tf.Tensor: shape=(8,), dtype=float64, numpy=array([ 3.,  6.,  9., 12., 15., 18., 21., 24.])>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check out X and Y\n",
    "X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d89c092c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 71ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[24.993067]], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Try and make a prediction using our model\n",
    "\n",
    "y_pred = model.predict([20.0])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c005cfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[32.493065]], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred + 7.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17970b51",
   "metadata": {},
   "source": [
    "## Improve a Model \n",
    "\n",
    "We can improve a 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 number of hidden units or Neurons. Change Activation function of each layers\n",
    "2. Compiling a model - Here we might change the optimization function or perhaps the learning rate of the optimization function\n",
    "3. Fitting a Model - Here we might a model for more epochs, train it for longer. Or provide more data for learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "995b3379",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1/1 [==============================] - 0s 200ms/step - loss: 14.1176 - mae: 14.1176\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 13.9851 - mae: 13.9851\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 13.8526 - mae: 13.8526\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 13.7201 - mae: 13.7201\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 13.5876 - mae: 13.5876\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 13.4551 - mae: 13.4551\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 13.3226 - mae: 13.3226\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 13.1901 - mae: 13.1901\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 13.0576 - mae: 13.0576\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 12.9251 - mae: 12.9251\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 12.7926 - mae: 12.7926\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 12.6601 - mae: 12.6601\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 12.5276 - mae: 12.5276\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 12.3951 - mae: 12.3951\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 12.2626 - mae: 12.2626\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 12.1301 - mae: 12.1301\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 11.9976 - mae: 11.9976\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 11.8651 - mae: 11.8651\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 11.7326 - mae: 11.7326\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 11.6001 - mae: 11.6001\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 11.4676 - mae: 11.4676\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 11.3351 - mae: 11.3351\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 11.2026 - mae: 11.2026\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 11.0701 - mae: 11.0701\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 10.9376 - mae: 10.9376\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 10.8051 - mae: 10.8051\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 10.6726 - mae: 10.6726\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 10.5401 - mae: 10.5401\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 10.4076 - mae: 10.4076\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 10.2751 - mae: 10.2751\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 10.1426 - mae: 10.1426\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 10.0101 - mae: 10.0101\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 9.8776 - mae: 9.8776\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 9.7451 - mae: 9.7451\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 9.6126 - mae: 9.6126\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 9.4801 - mae: 9.4801\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 9.3476 - mae: 9.3476\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 9.2151 - mae: 9.2151\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 9.0826 - mae: 9.0826\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.9501 - mae: 8.9501\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.8176 - mae: 8.8176\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.6851 - mae: 8.6851\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.5526 - mae: 8.5526\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.4201 - mae: 8.4201\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.2876 - mae: 8.2876\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.1551 - mae: 8.1551\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 8.0226 - mae: 8.0226\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 7.8901 - mae: 7.8901\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.7576 - mae: 7.7576\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 7.6251 - mae: 7.6251\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.4926 - mae: 7.4926\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.3601 - mae: 7.3601\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.2276 - mae: 7.2276\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.1025 - mae: 7.1025\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0969 - mae: 7.0969\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0912 - mae: 7.0912\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0856 - mae: 7.0856\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.0800 - mae: 7.0800\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0744 - mae: 7.0744\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0688 - mae: 7.0688\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0631 - mae: 7.0631\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0575 - mae: 7.0575\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0519 - mae: 7.0519\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0462 - mae: 7.0462\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0406 - mae: 7.0406\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0350 - mae: 7.0350\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0294 - mae: 7.0294\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0238 - mae: 7.0238\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0181 - mae: 7.0181\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0125 - mae: 7.0125\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0069 - mae: 7.0069\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 7.0012 - mae: 7.0012\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9956 - mae: 6.9956\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 6.9900 - mae: 6.9900\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9844 - mae: 6.9844\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9788 - mae: 6.9788\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9731 - mae: 6.9731\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9675 - mae: 6.9675\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9619 - mae: 6.9619\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9563 - mae: 6.9563\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.9506 - mae: 6.9506\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9450 - mae: 6.9450\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.9394 - mae: 6.9394\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 6.9338 - mae: 6.9338\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 6.9281 - mae: 6.9281\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9225 - mae: 6.9225\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 6.9169 - mae: 6.9169\n",
      "Epoch 88/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9113 - mae: 6.9113\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9056 - mae: 6.9056\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.9000 - mae: 6.9000\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.8944 - mae: 6.8944\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.8888 - mae: 6.8888\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.8831 - mae: 6.8831\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.8775 - mae: 6.8775\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.8719 - mae: 6.8719\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.8663 - mae: 6.8663\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.8606 - mae: 6.8606\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.8550 - mae: 6.8550\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.8494 - mae: 6.8494\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.8438 - mae: 6.8438\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1f6ad467730>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets rebuild our model\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model.compile(loss=tf.keras.losses.mae,\n",
    "             optimizer=tf.keras.optimizers.SGD(),\n",
    "             metrics=[\"mae\"])\n",
    "\n",
    "model.fit(tf.expand_dims(X, axis=-1),y, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fd813600",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 53ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[29.417727]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test prediction\n",
    "model.predict([17.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f9eb465a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1/1 [==============================] - 0s 391ms/step - loss: 13.5394 - mae: 13.5394\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 12.1370 - mae: 12.1370\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 10.9011 - mae: 10.9011\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 9.7831 - mae: 9.7831\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 8.4876 - mae: 8.4876\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 6.9069 - mae: 6.9069\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 4.9880 - mae: 4.9880\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 4.2445 - mae: 4.2445\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 5.1909 - mae: 5.1909\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5.9131 - mae: 5.9131\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.1184 - mae: 6.1184\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5.9264 - mae: 5.9264\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 5.4402 - mae: 5.4402\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 4.7675 - mae: 4.7675\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 4.1928 - mae: 4.1928\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.9008 - mae: 3.9008\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 4.2504 - mae: 4.2504\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 4.6709 - mae: 4.6709\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 4.6872 - mae: 4.6872\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 4.3873 - mae: 4.3873\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.9811 - mae: 3.9811\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 3.7612 - mae: 3.7612\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.9095 - mae: 3.9095\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 4.0310 - mae: 4.0310\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 4.2005 - mae: 4.2005\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 4.2461 - mae: 4.2461\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 4.0993 - mae: 4.0993\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 3.9622 - mae: 3.9622\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.8005 - mae: 3.8005\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.6698 - mae: 3.6698\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.7009 - mae: 3.7009\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.7861 - mae: 3.7861\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.8124 - mae: 3.8124\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 3.7879 - mae: 3.7879\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.7164 - mae: 3.7164\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.5928 - mae: 3.5928\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.4139 - mae: 3.4139\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.4794 - mae: 3.4794\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.5127 - mae: 3.5127\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 3.5432 - mae: 3.5432\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.4833 - mae: 3.4833\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.4236 - mae: 3.4236\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 3.3352 - mae: 3.3352\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.2224 - mae: 3.2224\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.0871 - mae: 3.0871\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.9261 - mae: 2.9261\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 2.9642 - mae: 2.9642\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.8934 - mae: 2.8934\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 2.7028 - mae: 2.7028\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 2.5248 - mae: 2.5248\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.4499 - mae: 2.4499\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.3695 - mae: 2.3695\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.1776 - mae: 2.1776\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.9834 - mae: 1.9834\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.8220 - mae: 1.8220\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.7505 - mae: 1.7505\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.3742 - mae: 1.3742\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 1.1149 - mae: 1.1149\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.9788 - mae: 0.9788\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.5696 - mae: 0.5696\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.6251 - mae: 0.6251\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6023 - mae: 0.6023\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4280 - mae: 0.4280\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.5777 - mae: 0.5777\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.6481 - mae: 0.6481\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.5074 - mae: 0.5074\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.6197 - mae: 0.6197\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.5710 - mae: 0.5710\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.4221 - mae: 0.4221\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.7604 - mae: 0.7604\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.5358 - mae: 0.5358\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6836 - mae: 0.6836\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.8381 - mae: 0.8381\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.3864 - mae: 0.3864\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.5130 - mae: 0.5130\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.5238 - mae: 0.5238\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.2606 - mae: 0.2606\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.3576 - mae: 0.3576\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.3094 - mae: 0.3094\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.2516 - mae: 0.2516\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4895 - mae: 0.4895\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.3254 - mae: 0.3254\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.6079 - mae: 0.6079\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6477 - mae: 0.6477\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.2071 - mae: 0.2071\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.1875 - mae: 0.1875\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.5329 - mae: 0.5329\n",
      "Epoch 88/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4661 - mae: 0.4661\n",
      "Epoch 89/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4131 - mae: 0.4131\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4488 - mae: 0.4488\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.3101 - mae: 0.3101\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.2071 - mae: 0.2071\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.5222 - mae: 0.5222\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4734 - mae: 0.4734\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.2242 - mae: 0.2242\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.2946 - mae: 0.2946\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.6106 - mae: 0.6106\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4303 - mae: 0.4303\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4521 - mae: 0.4521\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4975 - mae: 0.4975\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1f6ba50e910>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets rebuild our model\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(1024, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(512, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(256, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(1),\n",
    "])\n",
    "\n",
    "model.compile(loss=tf.keras.losses.mae,\n",
    "             optimizer=tf.keras.optimizers.Adam(),\n",
    "             metrics=[\"mae\"])\n",
    "\n",
    "model.fit(tf.expand_dims(X, axis=-1),y, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7fe05abf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 69ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[26.10041 ],\n",
       "       [29.288012],\n",
       "       [32.589027]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([17.0, 20.0, 23.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac1411b",
   "metadata": {},
   "source": [
    "## Evaluating a Model\n",
    "\n",
    "In practice, a typical workflow you'll go through when building a neural network is:\n",
    "\n",
    "```\n",
    "Build a model -> fit it -> evaluate it - > tweak a model ->\n",
    "fit it -> evaluate it -> tweak a model -> fit it -> evaluate it...\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac591fb",
   "metadata": {},
   "source": [
    "When it comes to evualiation....there are 3 words you should memorize:\n",
    "```\n",
    "Visualize, Visualize, Visualize\n",
    "```\n",
    "Is a good idea to visualize:\n",
    "The Data - What data are we working with. What does it look like?\n",
    "The Model itself - What does are model look like\n",
    "The training of a model - how does a model perform while trains\n",
    "The predictions of the model - how do the predictions of a model line up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "51eb9695",
   "metadata": {},
   "outputs": [
    {
     "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])>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Testing with a new model and prediction\n",
    "\n",
    "X = tf.range(-100, 100, 4)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eb98e2f7",
   "metadata": {},
   "outputs": [
    {
     "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])>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make labels for the Datasets\n",
    "\n",
    "y = X + 10\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "15ba5a58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14eb9c56f40>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the data\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f95ffd",
   "metadata": {},
   "source": [
    "### The 3 sets...\n",
    "\n",
    "* **Training set** - Model learns from this data typically 70-80% of the total data you have\n",
    "* **Validation set** - The model gets tuned on this data, which is typically 10-15% of available data\n",
    "* **Test Set** - The Model gets evaluated on this data to test what it learned. This set is typically 10-15% of the total data available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f49cf134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the lenght of how many samples we have\n",
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c332706c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 10, 10, 40)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the Data into train and test\n",
    "\n",
    "X_train = X[:40] # frist 40 are training samples (80% of the data)\n",
    "y_train = y[:40]\n",
    "\n",
    "X_test = X[40:] # last 10 are testing samples (20% of the Data)\n",
    "y_test = y[40:]\n",
    "\n",
    "len(X_train), len(X_test), len(y_test), len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c9ae6e",
   "metadata": {},
   "source": [
    "### Visualizing the Data\n",
    "\n",
    "Now we got our data split. Let's visualize it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "46f6740d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rVulLX/qSVq1aperqah1//PHxmBkzZqiiokIvvPBCj8mVJO3atSvhDElHR0c2voWUFTM88qhxaaPqJ9Tn7MNx00036Y477lA4bGq2jx8/Xn/961/1k5/8RJdccona2tp0+OGH68QTT5TH49HYsWPjz44YMUKSVF1drZqamv3+OQceeKD+67/+S16vVxMnTtQZZ5yhFStWaPbs2Xr99de1fPlyrV69Ov7f7r777tPhhx/e5+9r4sSJ+t///d/49Ve+8pX47w855BDdddddOuGEE7Rz504NHTpUBx10kCRp5MiRqq6ujseee+65Ca/7wAMPaMSIEfrrX/+qI488ss/zAwAAKEXhSWG1nNfSY5+gpllNafUJKlZZSYI2b94sSRo1alTC+KhRo+L3Nm/erJEjRyZOZsAAHXTQQfGYnixYsEC33HJLhmecrDcVM3LRIOqDDz7Qm2++qcsvv1yzZ8+Oj+/du1dVVVWSzFa2L3zhC5owYYJmzZqlf/qnf9Kpp57a6z/riCOOkNf7j8TO7/frlVdekWRWXwYMGKDjjjsufv+www7TgQce2NdvTa7ryuP5x79ArFmzRjfffLP+8pe/6P3334+v5LS1tekzn/nMPl/njTfe0I033qgXXnhBf//73xOeIwkCAAClzul0FGmLyN5hyz/Mr9CYUMp/rA9PCqt+Qn2vnyt2WUmCsmn+/PmaN29e/Lqjo0PBYDDjf06hVcyIFYz46U9/qilTpiTciyUsxx13nDZs2KAnn3xSy5cv13nnnacZM2aopaWlV3/WwIEDE649Hk9Wt5S1trZq/PjxkkyyN3PmTM2cOVMPPfSQRowYoba2Ns2cOVO7d+/e7+uceeaZGjt2rH76059q9OjR6uzs1JFHHpnyOQAAgGJntVo9rug0z2pOuaLjrfDm5B/1C0lWkqDYdqstW7bI7/9HRYktW7bED+HX1NQknSPZu3ev3nvvvf1u16qsrFRlZWXmJ91NoVXMGDVqlEaPHq233npLF1100T7jfD6fzj//fJ1//vlqaGjQrFmz9N577+mggw7SwIED5Tj9q2w3YcIE7d27Vy+99JImT54sSVq/fr3ef//9Pr3e66+/rqVLl2r+/Pnx63fffVcLFy6MJ7d//vOfE54ZNGiQJCV8L++++67WrVunn/70pwqFzAG+Z599tk9zAgAAKCaxc+zdj3HEzrG3nNdS0lvb+iIrfYLGjx+vmpqahP4zHR0deuGFFzR16lRJ0tSpU7Vt2zatWbMmHvPUU0+ps7MzaaUjHwqxYsYtt9yiBQsW6K677tLf/vY3vfLKK1q0aJHuvPNOSdKdd96pX/ziF3r99df1t7/9TY899phqamri52bGjRunFStWaPPmzX1OWiZOnKgZM2boiiuu0J/+9Ce99NJLuuKKKzR48OCELW092bt3rzZv3qxNmzbplVde0d13362TTjpJxxxzjL7xjW9IksaMGaNBgwbp7rvv1ltvvaUnnnhC3/3udxNeZ+zYsfJ4PPrtb3+r//u//9POnTt14IEHavjw4br33nu1fv16PfXUUwkrhgAAAKUo1Tl2SWpc2pjVFi/FqM9J0M6dO7V27VqtXbtWkimGsHbtWrW1tcnj8aixsVH/8R//oSeeeEKvvPKKLr74Yo0ePVpnn322JGnSpEmaNWuWZs+erT/96U967rnndNVVV+lLX/qSRo8enYnvrV8KsWLGV7/6Vd13331atGiRjjrqKJ100klavHhxfCvZsGHDdOutt+r444/XCSecoI0bN+p3v/udKirMf+Y77rhDy5YtUzAY1LHHHtvnefz3f/+3Ro0apenTp+ucc87R7NmzNWzYMB1wwAH7fe61116T3+/XmDFjVFdXpyVLlmj+/PmKRCIaOnSoJFPAYfHixXrsscf0mc98RgsXLtTtt9+e8Dq1tbW65ZZbdMMNN2jUqFG66qqrVFFRoUceeURr1qzRkUceqWuuuUa33XZbn79HAACAYtCbc+z4B4/ruslpYxpWrlwZ79fS1SWXXKLFixfLdV3ddNNNuvfee7Vt2zadeOKJ+uEPf6hPf/rT8dj33ntPV111lX7zm9+ooqJC5557ru666674D8Tp6OjoUFVVlbZv3y6fz5dw7+OPP9aGDRs0fvz4lD+g70tP+yuDvmDJV8zojXfeeUfBYFDLly/XKaecku/pZEQmPjsAAADZ9otXfqELrQtTxj0cflgXHHVBDmaUX/vLDbrqcxJUKLKdBEl9q7RRyp566int3LlTRx11lGzb1je/+U1Fo1H97W9/SyqqUKxIggAAQDFYuXGlTn4weWGiu6cvebosih+kmwQVXXW4fCjHihn7s2fPHv3bv/2b3nrrLQ0bNkzTpk3TQw89VDIJEAAAQLGInWOPdkR7PBfkkUcBXyCn59iLAUkQei1WwhoAAAD5FTvH3rCkQR55EhKhfJ1jLwZZqQ4HAAAAIDfCk8JqOa9Ftb7ahPGAL0B57H0oi5WgIj/2hDzgMwMAAPKhr2fRw5PCqp9Qzzn2NJV0EhQ7o/Lhhx9q8ODBeZ4NismHH34oSZxzAgAAOdNTVeKAL6DmWc1preZwjj19JZ0Eeb1eVVdXa+vWrZKkIUOGpGzoifLmuq4+/PBDbd26VdXV1fJ6+dcTAACQfVarpYYlDUnFDaIdUTUsaWBbW4aVdBIkSTU1NZIUT4SAdFRXV8c/OwAAANnkdDqau3Ruj9XdXLnyyKPGpY2qn1DP9rYMKfkkyOPxyO/3a+TIkdqzZ0++p4MiMHDgQFaAAABAzkTaIglb4Lpz5aq9o12Rtgjb3TKk5JOgGK/Xyw+2AAAAKDj2DjujcUiNEtkAAABAHvmH+TMah9RIggAAAIA8Co0JKeALxJubdueRR0FfUKExoRzPrHSRBAEAAAB55K3wqnlWsyQlJUKx66ZZTRRFyCCSIAAAACDPwpPCajmvRbW+2oTxgC9Aeews8Lium1yLr4h0dHSoqqpK27dvl8/ny/d0AAAAADmdjiJtEdk7bPmH+RUaE0prJaevz8FINzcom+pwAAAAQC5YrZbmLp2bUPY64AuoeVZzyhUdb4WXMtg5wHY4AAAAIEOsVksNSxqS+v5EO6JqWNIgq9XK08zQFUkQAAAAkAFOp6O5S+fKVfJpk9hY49JGOZ1OrqeGbkiCAAAAgAyItEWSVoC6cuWqvaNdkbZIDmeFnpAEAQAAABlg77AzGofsIQkCAAAAMsA/zJ/ROGQPSRAAAACQAaExIQV8gaSGpzEeeRT0BRUaE8rxzNAdSRAAAACQAd4Kr5pnNUtSUiIUu26a1UTfnwJAEgQAAABkSHhSWC3ntajWV5swHvAF1HJeS8o+QcgNj+u6yTX8iki6XWEBAACA3nI6HUXaIrJ32PIP8ys0JpTWSk5fn0P/pJsbDMjhnAAAAICiYbVamrt0bkLZ64AvoOZZzSlXdLwVXtWNq8vyDNFXbIcDAAAAurFaLTUsaUjq+xPtiKphSYOsVitPM0MmkAQBAAAAXTidjuYunStXyadGYmONSxvldDq5nhoyhCQIAAAA6CLSFklaAerKlav2jnZF2iI5nBUyiSQIAAAA6MLeYWc0DoWHJAgAAADowj/Mn9E4FB6SIAAAAKCL0JiQAr5AUsPTGI88CvqCCo0J5XhmyBSSIAAAAKALb4VXzbOaJSkpEYpdN81qou9PESMJAgAAALoJTwqr5bwW1fpqE8YDvoBazmtJ2ScIhc3jum5y7b8ikm5XWAAAAJQnp9NRpC0ie4ct/zC/QmNCaa/i9OdZ5F66ucGAHM4JAAAAyCmr1dLcpXMTSl4HfAE1z2pOazXHW+FV3bi6LM4Q+cB2OAAAAJQkq9VSw5KGpJ4/0Y6oGpY0yGq18jQz5BtJEAAAAEqO0+lo7tK5cpV88iM21ri0UU6nk+upoQCQBAEAAKDkRNoiSStAXbly1d7RrkhbJIezQqEgCQIAAEDJsXfYGY1DaSEJAgAAQMnxD/NnNA6lhSQIAAAAJSc0JqSAL5DU7DTGI4+CvqBCY0I5nhkKAUkQAAAASo63wqvmWc2SlJQIxa6bZjXR86dMkQQBAACgJIUnhdVyXotqfbUJ4wFfQC3ntaTVJwilyeO6bnLdwCKSbldYAAAAFDen01GkLSJ7hy3/ML9CY0JpreT09TkUn3RzgwE5nBMAAADQJ1arpblL5yaUvQ74Amqe1ZxyRcdb4VXduLoszxDFhO1wAAAAKGhWq6WGJQ1JfX+iHVE1LGmQ1WrlaWYoViRBAAAAKFhOp6O5S+fKVfIJjthY49JGOZ1OrqeGIkYSBAAAgIIVaYskrQB15cpVe0e7Im2RHM4KxY4kCAAAAAXL3mFnNA6QSIIAAABQwPzD/BmNAySSIAAAABSw0JiQAr5AUsPTGI88CvqCCo0J5XhmKGZZTYLGjRsnj8eT9DVnzhxJUl1dXdK9K6+8MptTAgAAQBHxVnjVPKtZkpISodh106wm+v6gV7KaBK1evVq2bce/li1bJkn64he/GI+ZPXt2Qsytt96azSkBAACgyIQnhdVyXotqfbUJ4wFfQC3ntaTsEwR0l9VmqSNGjEi4XrhwoQ499FCddNJJ8bEhQ4aopqYmm9MAAABAAXE6HUXaIrJ32PIP8ys0JpRyJSc8Kaz6CfW9fg7oSVaToK52796tn//855o3b548nn8sZT700EP6+c9/rpqaGp155pn69re/rSFDhuzzdXbt2qVdu3bFrzs6OrI6bwAAAGSO1Wpp7tK5CWWvA76Ammc1p1zR8VZ4VTeuLsszRDnIWRL0+OOPa9u2bbr00kvjYxdeeKHGjh2r0aNH6+WXX9b111+vdevWybL23fV3wYIFuuWWW3IwYwAAAGSS1WqpYUlDUuPTaEdUDUsa2NqGnPG4rpvcfjcLZs6cqUGDBuk3v/nNPmOeeuopnXLKKVq/fr0OPfTQHmN6WgkKBoPavn27fD5fxucNAACA/nM6HY1rHrfPxqceeRTwBbRh7ga2uKHPOjo6VFVVlTI3yEmJ7LffflvLly/XV7/61f3GTZkyRZK0fv36fcZUVlbK5/MlfAEAAKCwRdoi+0yAJMmVq/aOdkXaIjmcFcpVTpKgRYsWaeTIkTrjjDP2G7d27VpJkt9PsysAAIBSYu+wMxoH9EfWzwR1dnZq0aJFuuSSSzRgwD/+uDfffFMPP/ywTj/9dA0fPlwvv/yyrrnmGk2fPl1HH310tqcFAACAHPIPS+8fudONA/oj60nQ8uXL1dbWpq985SsJ44MGDdLy5cvV1NSkDz74QMFgUOeee66+9a1vZXtKAAAAyLHQmJACvoCiHdGkwgjSP84EhcaE8jA7lJucFUbIlnQPPwEAACC/YtXhJCUkQh6Z9ilUh0N/FVRhBAAAACA8KayW81pU66tNGA/4AiRAyClWggAAANBrTqejSFtE9g5b/mF+hcaE0i5t3Z9ngf1JNzfIWbNUAAAAlAar1dLcpXMTSl4HfAE1z2pOazXHW+FV3bi6LM4Q2D+2wwEAACBtsXM93Xv+RDuialjSIKvVytPMgPSRBAEAACAtTqejuUvn9ljdLTbWuLRRTqeT66kBvUISBAAAgLRE2iJJK0BduXLV3tGuSFskh7MCeo8kCAAAAGmxd9gZjQPyhSQIAAAAafEP82c0DsgXkiAAAACkJTQmpIAvEG9u2p1HHgV9QYXGhHI8M6B3SIIAAACQFm+FV82zmiUpKRGKXTfNaqLnDwoeSRAAAADSFp4UVst5Lar11SaMB3wBtZzXklafICDfPK7rJtc4LCLpdoUFAABAMseRIhHJtiW/XwqFJG8aCzlOp6NIW0T2Dlv+YX6FxoRYAULepZsbDMjhnAAAAFBALEuaO1d6p0vV60BAam6WwikWdLwVXtWNq8vq/IBsYTscAABAGbIsqaEhMQGSpGjUjFtWfuYF5AJJEAAAQJlxHLMC1NOhiNhYY6OJA0oRSRAAAECZiUSSV4C6cl2pvd3EAaWIJAgAAKDM2HZm44BiQxIEAABQZvz+zMYBxYYkCAAAoMyEQqYKnMfT832PRwoGTRxQikiCAAAAyozXa8pgS8mJUOy6qSm9fkFAMSIJAgAAKEPhsNTSItXWJo4HAmY8VZ8goJjRLBUAAKDIOY6p5Gbb5hxPKJTeKk44LNXX9+1ZoJiRBAEAABQxyzI9f7qWvA4EzHa3dFZzvF6pri5r0wMKEtvhAAAAipRlSQ0NyT1/olEzbln5mRdQ6EiCAAAAipDjmBUg102+FxtrbDRxABKRBAEAABShSCR5Bagr15Xa200cgEQkQQAAAEXItjMbB5QTkiAAAIAi5PdnNg4oJyRBAAAARSgUMlXgujc7jfF4pGDQxAFIRBIEAABQhLxeUwZbSk6EYtdNTfT8AXpCEgQAAFCkwmGppUWqrU0cDwTMeDp9goByRLNUAACAAuE4ppqbbZuzPKFQ6pWccFiqr+/9c0A5IwkCAAAoAJZl+v50LXsdCJgtb6lWdLxeqa4uq9MDSgrb4QAAAPLMsqSGhuS+P9GoGbes/MwLKFUkQQAAAHnkOGYFyHWT78XGGhtNHIDMIAkCAADIo0gkeQWoK9eV2ttNHIDMIAkCAADII9vObByA1EiCAAAA8sjvz2wcgNRIggAAAPIoFDJV4Lo3PI3xeKRg0MQByAySIAAAgDzyek0ZbCk5EYpdNzXR9wfIJJIgAACAPAuHpZYWqbY2cTwQMOOp+gQB6B2apQIAAGSY45hqbrZtzvKEQqlXcsJhqb6+988B6D2SIAAAgAyyLNP3p2vZ60DAbHlLtaLj9Up1dVmdHgCxHQ4AACBjLEtqaEju+xONmnHLys+8ACQiCQIAAMgAxzErQK6bfC821tho4gDkF0kQAABABkQiyStAXbmu1N5u4gDkF0kQAABABth2ZuMAZA9JEAAAQAb4/ZmNA5A9JEEAAAAZEAqZKnDdG57GeDxSMGjiAOQXSRAAAEAGeL2mDLaUnAjFrpua6PsDFAKSIAAAgAwJh6WWFqm2NnE8EDDjqfoEAcgNmqUCAAD0wHFMJTfbNud4QqH0VnHCYam+vm/PAsiNrK4E3XzzzfJ4PAlfEydOjN//+OOPNWfOHA0fPlxDhw7Vueeeqy1btmRzSgAAAClZljRunHTyydKFF5pfx41Lv9mp1yvV1UkXXGB+JQECCkvWt8MdccQRsm07/vXss8/G711zzTX6zW9+o8cee0zPPPOMNm3apDDrxAAAII8sS2poSO75E42a8XQTIQCFK+vb4QYMGKCampqk8e3bt+v+++/Xww8/rP/3//6fJGnRokWaNGmSnn/+ef1//9//l+2pAQAAJHAcae5c09i0O9c1BQ4aG812N1Z3gOKV9ZWgN954Q6NHj9Yhhxyiiy66SG1tbZKkNWvWaM+ePZoxY0Y8duLEiRozZoxWrVq1z9fbtWuXOjo6Er4AAAAyIRJJXgHqynWl9nYTB6B4ZTUJmjJlihYvXqylS5fqRz/6kTZs2KBQKKQdO3Zo8+bNGjRokKqrqxOeGTVqlDZv3rzP11ywYIGqqqriX8FgMJvfAgAAKCO2ndk4AIUpq9vhTjvttPjvjz76aE2ZMkVjx47VkiVLNHjw4D695vz58zVv3rz4dUdHB4kQAADICL8/s3EAClNO+wRVV1fr05/+tNavX6+amhrt3r1b27ZtS4jZsmVLj2eIYiorK+Xz+RK+AAAAMiEUMj19ujc7jfF4pGDQxAEoXjlNgnbu3Kk333xTfr9fkydP1sCBA7VixYr4/XXr1qmtrU1Tp07N5bQAAAAkmWIHzc3m990Todh1UxNFEYBil9Uk6LrrrtMzzzyjjRs36o9//KPOOecceb1eXXDBBaqqqtLll1+uefPm6emnn9aaNWt02WWXaerUqVSGAwAAeRMOSy0tUm1t4nggYMbp5gEUv6yeCXrnnXd0wQUX6N1339WIESN04okn6vnnn9eIESMkST/4wQ9UUVGhc889V7t27dLMmTP1wx/+MJtTAgAAZcZxTDU32zZneUKh1Cs54bApg93b5wAUB4/r9lQJv3h0dHSoqqpK27dv53wQAABIYFmm70/XsteBgNnyxooOUHrSzQ1yeiYIAAAgVyxLamhI7vsTjZpxy8rPvADkH0kQAAAoOY5jVoB62u8SG2tsNHEAyg9JEAAAKDmRSPIKUFeuK7W3mzgA5YckCAAAlBzbzmwcgNJCEgQAAEqO35/ZOAClhSQIAACUnFDIVIHr3vA0xuORgkETB6D8kAQBAICS4/WaMthSciIUu25qou8PUK5IggAAQEkKh6WWFqm2NnE8EDDj9AkCyteAfE8AAAAgHY5jqrnZtjnLEwqlXskJh6X6+t4/B6C0kQQBAICCZ1mm70/XsteBgNnylmpFx+uV6uqyOj0ARYbtcAAAoKBZltTQkNz3Jxo145aVn3kBKF4kQQAAoGA5jlkBct3ke7GxxkYTBwDpIgkCAAAFKxJJXgHqynWl9nYTBwDpIgkCAAAFy7YzGwcAEkkQAAAoYH5/ZuMAQCIJAgAABSwUMlXgujc8jfF4pGDQxAFAukiCAABAwfJ6TRlsKTkRil03NdH3B0DvkAQBAICCFg5LLS1SbW3ieCBgxlP1CQKA7miWCgAAcsZxTCU32zbneEKh9FZxwmGpvr5vzwJAdyRBAAAgJyzL9PzpWvI6EDDb3dJZzfF6pbq6rE0PQBlhOxwAAMg6y5IaGpJ7/kSjZtyy8jMvAOWJJAgAAGSV45gVINdNvhcba2w0cQCQCyRBAAAgqyKR5BWgrlxXam83cQCQCyRBAAAgq2w7s3EA0F8kQQAAIKv8/szGAUB/kQQBAICsCoVMFbjuzU5jPB4pGDRxAJALJEEAACCrvF5TBltKToRi101N9PwBkDskQQAAIOvCYamlRaqtTRwPBMx4On2CACBTaJYKAAB6zXFMNTfbNmd5QqHUKznhsFRf3/vnACDTSIIAAECvWJbp+9O17HUgYLa8pVrR8XqlurqsTg8AUmI7HAAASJtlSQ0NyX1/olEzbln5mRcA9AZJEAAASIvjmBUg102+FxtrbDRxAFDISIIAAEBaIpHkFaCuXFdqbzdxAFDISIIAAEBabDuzcQCQLyRBAAAgLX5/ZuMAIF9IggAAQFpCIVMFrnvD0xiPRwoGTRwAFDKSIAAAkBav15TBlpITodh1UxN9fwAUPpIgAACQtnBYammRamsTxwMBM56qTxAAFAKapQIAUKYcx1Rys21zjicUSm8VJxyW6uv79iwAFAKSIAAAypBlmZ4/XUteBwJmu1s6qzler1RXl7XpAUBWsR0OAIAyY1lSQ0Nyz59o1IxbVn7mBQC5QhIEAEAZcRyzAuS6yfdiY42NJg4AShVJEAAAZSQSSV4B6sp1pfZ2EwcApYokCACAMmLbmY0DgGJEEgQAQBnx+zMbBwDFiCQIAIAyEgqZKnDdm53GeDxSMGjiAKBUkQQBAFBGvF5TBltKToRi101N9PwBUNpIggAAKDPhsNTSItXWJo4HAmY8nT5BAFDMaJYKAECRcxxTzc22zVmeUCj1Sk44LNXX9/45ACgFJEEAABQxyzJ9f7qWvQ4EzJa3VCs6Xq9UV5fV6QFAQWI7HAAARcqypIaG5L4/0agZt6z8zAsACl1Wk6AFCxbohBNO0LBhwzRy5EidffbZWrduXUJMXV2dPB5PwteVV16ZzWkBAFD0HMesALlu8r3YWGOjiQMAJMpqEvTMM89ozpw5ev7557Vs2TLt2bNHp556qj744IOEuNmzZ8u27fjXrbfems1pAQBQ9CKR5BWgrlxXam83cQCARFk9E7R06dKE68WLF2vkyJFas2aNpk+fHh8fMmSIampqsjkVAABKim1nNg4AyklOzwRt375dknTQQQcljD/00EM6+OCDdeSRR2r+/Pn68MMP9/kau3btUkdHR8IXAADlxu/PbBwAlJOcVYfr7OxUY2OjPv/5z+vII4+Mj1944YUaO3asRo8erZdfflnXX3+91q1bJ2sfpzkXLFigW265JVfTBgCgIIVCpgpcNNrzuSCPx9wPhXI/NwAodB7X7emvzsz72te+pieffFLPPvusAoHAPuOeeuopnXLKKVq/fr0OPfTQpPu7du3Srl274tcdHR0KBoPavn27fD5fVuYOAEAhilWHkxITIY/H/ErjUwDlpqOjQ1VVVSlzg5xsh7vqqqv029/+Vk8//fR+EyBJmjJliiRp/fr1Pd6vrKyUz+dL+AIAoByFwybRqa1NHA8ESIAAYH+yuh3OdV1dffXV+tWvfqWVK1dq/PjxKZ9Zu3atJMnPJmYAQJlxHFPNzbbNWZ5QyDQ03Z9wWKqv7/1zAFDOspoEzZkzRw8//LB+/etfa9iwYdq8ebMkqaqqSoMHD9abb76phx9+WKeffrqGDx+ul19+Wddcc42mT5+uo48+OptTAwCgoFiW6fvTtex1ICA1N6de0fF6pbq6rE4PAEpKVs8EeWKbkrtZtGiRLr30UrW3t+uf//mf9eqrr+qDDz5QMBjUOeeco29961tpb3NLd98fAACFKna2p/v/I3O2BwB6J93cIGeFEbKFJAgAUMwcRxo3bt+NT2NV3jZsYIsbAKRSUIURAABAzyKRfSdAklkdam83cQCAzCAJAgAgj2w7s3EAgNRIggAAyKN0i6FSNBUAMockCACAPAqFzJmffdQSkscjBYMmDgCQGSRBAADkkddrymBLyYlQ7LqpiaIIAJBJJEEAAORZOGzKYNfWJo4HApTHBoBsyGqzVAAAyo3jmEputm3O8YRC6a3ihMNSfX3fngUA9A5JEAAAGWJZ0ty5iSWvAwGz3S2d1RyvV6qry9r0AACfYDscAAAZYFlSQ0Nyz59o1IxbVn7mBQBIRhIEAEA/OY5ZAXLd5HuxscZGEwcAyD+SIAAA+ikSSV4B6sp1pfZ2EwcAyD+SIAAA+sm2MxsHAMgukiAAAPrJ789sHAAgu0iCAADop1DIVIHr3uw0xuORgkETBwDIP5IgAAD6yes1ZbCl5EQodt3URM8fACgUJEEAAGRAOCy1tEi1tYnjgYAZT6dPEAAgN2iWCgBADxzHVHOzbXOWJxRKvZITDkv19b1/DgCQWyRBAAB0Y1mm70/XsteBgNnylmpFx+uV6uqyOj0AQD+xHQ4AgC4sS2poSO77E42accvKz7wAAJlDEgQAwCccx6wAuW7yvdhYY6OJAwAUL5IgAAA+EYkkrwB15bpSe7uJAwAUL5IgAAA+YduZjQMAFCaSIAAAPuH3ZzYOAFCYSIIAAPhEKGSqwHVveBrj8UjBoIkDABQvkiAAAD7h9Zoy2FJyIhS7bmqi7w8AFDuSIAAAugiHpZYWqbY2cTwQMOOp+gQBAAofzVIBACXNcUw1N9s2Z3lCodQrOeGwVF/f++cAAMWBJAgAULIsy/T96Vr2OhAwW95Sreh4vVJdXVanBwDIE7bDAQBKkmVJDQ3JfX+iUTNuWfmZFwAg/0iCAAAlx3HMCpDrJt+LjTU2mjgAQPkhCQIAlJxIJHkFqCvXldrbTRwAoPyQBAEASo5tZzYOAFBaSIIAACXH789sHACgtJAEAQBKTihkqsB1b3ga4/FIwaCJAwCUH5IgAEDJ8XpNGWwpORGKXTc10fcHAMoVSRAAoCSFw1JLi1RbmzgeCJjxVH2CAACli2apAICC5zimkpttm3M8oVB6qzjhsFRf37dnAQCliyQIAFDQLMv0/Ola8joQMNvd0lnN8XqlurqsTQ8AUITYDgcAKFiWJTU0JPf8iUbNuGXlZ14AgOJGEgQAKEiOY1aAXDf5XmyssdHEAQDQGyRBAICCFIkkrwB15bpSe7uJAwCgN0iCAAAFybYzGwcAQAxJEACgIPn9mY0DACCGJAgAUJBCIVMFrnuz0xiPRwoGTRwAAL1BEgQAKEherymDLSUnQrHrpiZ6/gAAeo8kCABQsMJhqaVFqq1NHA8EzHg6fYIAAOiOZqkAgJxxHFPNzbbNWZ5QKPVKTjgs1df3/jkAAPaFJAgAkBOWZfr+dC17HQiYLW+pVnS8XqmuLqvTAwCUEbbDAQCyzrKkhobkvj/RqBm3rPzMCwBQnkiCAABZ5ThmBch1k+/FxhobTRwAALlAEgQAyKpIJHkFqCvXldrbTRwAALlAEgQAyCrbzmwcAAD9VRBJ0D333KNx48bpgAMO0JQpU/SnP/0p31MCAGSI35/ZOAAA+ivvSdCjjz6qefPm6aabbtKLL76oz372s5o5c6a2bt2a76kBADIgFDJV4Lo3PI3xeKRg0MQBAJALeU+C7rzzTs2ePVuXXXaZPvOZz+jHP/6xhgwZogceeCDfUwMAZIDXa8pgS8mJUOy6qYm+PwCA3MlrErR7926tWbNGM2bMiI9VVFRoxowZWrVqVY/P7Nq1Sx0dHQlfAIDCFg5LLS1SbW3ieCBgxlP1CQIAIJPy2iz173//uxzH0ahRoxLGR40apddff73HZxYsWKBbbrklF9MDAPTAcUwlN9s253hCofRWccJhqb6+b88CAJBJeU2C+mL+/PmaN29e/Lqjo0PBYDCPMwKA8mFZpudP15LXgYDZ7pbOao7XK9XVZW16AACkJa9J0MEHHyyv16stW7YkjG/ZskU1NTU9PlNZWanKyspcTA8A0IVlSQ0NyU1Po1EzzrY2AECxyOuZoEGDBmny5MlasWJFfKyzs1MrVqzQ1KlT8zgzAEBXjmNWgLonQNI/xhobTRwAAIUu79Xh5s2bp5/+9Kd68MEH1draqq997Wv64IMPdNlll+V7agCAT0QiiVvgunNdqb3dxAEAUOjyfibo/PPP1//93//pxhtv1ObNm3XMMcdo6dKlScUSAAD5Y9uZjQMAIJ/yngRJ0lVXXaWrrroq39MAAOyD35/ZOAAA8inv2+EAAIUvFDJV4Lo3O43xeKRg0MQBAFDoSIIAACl5vaYMtpScCMWum5ro+QMAKA4kQQCAtITDpgx2bW3ieCBAeWwAQHEpiDNBAIDccxxTzc22zVmeUCj1Sk44LNXX9/45AAAKCUkQAJQhyzJ9f7qWvQ4EzJa3VCs6Xq9UV5fV6QEAkFVshwOAMmNZUkNDct+faNSMW1Z+5gUAQK6QBAFAGXEcswLkusn3YmONjSYOAIBSRRIEAGUkEkleAerKdaX2dhMHAECpIgkCgDJi25mNAwCgGJEEAUAZ8fszGwcAQDEiCQKAMhIKmSpw3Ruexng8UjBo4gAAKFUkQQBQRrxeUwZbSk6EYtdNTfT9AQCUNpIgACgz4bDU0iLV1iaOBwJmPFWfIAAAih3NUgGgyDmOqeZm2+YsTyiUeiUnHJbq63v/HAAApYAkCACKmGWZvj9dy14HAmbLW6oVHa9XqqvL6vQAAChIbIcDgCJlWVJDQ3Lfn2jUjFtWfuYFAEChIwkCgCLkOGYFyHWT78XGGhtNHAAASEQSBABFKBJJXgHqynWl9nYTBwAAEpEEAUARsu3MxgEAUE5IggCgCPn9mY0DAKCckAQBQBEKhUwVuO4NT2M8HikYNHEAACARSRAAFCGv15TBlpITodh1UxN9fwAA6AlJEAAUqXBYammRamsTxwMBM56qTxAAAOWKZqkAUAAcx1Rys21zjicUSm8VJxyW6uv79iwAAOWKJAgA8syyTM+friWvAwGz3S2d1RyvV6qry9r0AAAoOWyHA4A8siypoSG55080asYtKz/zAgCglJEEAUCeOI5ZAXLd5HuxscZGEwcAADKHJAgA8iQSSV4B6sp1pfZ2EwcAADKHJAgA8sS2MxsHAADSQxIEAHni92c2DgAApIckCADyJBQyVeC6NzuN8XikYNDEAQCAzCEJAoA88XpNGWwpORGKXTc10fMHAIBMIwkCgDwKh6WWFqm2NnE8EDDj6fQJAgAAvUOzVADIIMcx1dxs25zlCYVSr+SEw1J9fe+fAwAAfUMSBAAZYlmm70/XsteBgNnylmpFx+uV6uqyOj0AAPAJtsMBQAZYltTQkNz3Jxo145aVn3kBAIBkJEEA0E+OY1aAXDf5XmyssdHEAQCA/CMJAoB+ikSSV4C6cl2pvd3EAQCA/CMJAoB+su3MxgEAgOwiCQKAfvL7MxsHAACyiyQIAPopFDJV4Lo3PI3xeKRg0MQBAID8IwkCgH7yek0ZbCk5EYpdNzXR9wcAgEJBEgQAGRAOSy0tUm1t4nggYMZT9QkCAAC5Q7NUAOiB45hqbrZtzvKEQqlXcsJhqb6+988BAIDcIgkCgG4sy/T96Vr2OhAwW95Sreh4vVJdXVanBwAA+ontcADQhWVJDQ3JfX+iUTNuWfmZFwAAyBySIAD4hOOYFSDXTb4XG2tsNHEAAKB4kQQBwCcikeQVoK5cV2pvN3EAAKB4kQQBwCdsO7NxAACgMJEEAcAn/P7MxgEAgMJEEgQAnwiFTBW47g1PYzweKRg0cQAAoHhlJQnauHGjLr/8co0fP16DBw/WoYceqptuukm7d+9OiPF4PElfzz//fDamBAApeb2mDLaUnAjFrpua6PsDAECxy0qfoNdff12dnZ36yU9+osMOO0yvvvqqZs+erQ8++EC33357Quzy5ct1xBFHxK+HDx+ejSkBQFrCYamlpec+QU1NqfsEAQCAwudx3Z6KwWbebbfdph/96Ed66623JJmVoPHjx+ull17SMccc0+fX7ejoUFVVlbZv3y6fz5eh2QIoBY5jKrnZtjnHEwqlv4rTn2cBAEB+pJsbZGUlqCfbt2/XQQcdlDR+1lln6eOPP9anP/1pffOb39RZZ52139fZtWuXdu3aFb/u6OjI+FwBFD/L6nk1p7k5vdUcr1eqq8va9AAAQB7lpDDC+vXrdffdd+tf/uVf4mNDhw7VHXfcoccee0z/8z//oxNPPFFnn322nnjiif2+1oIFC1RVVRX/CgaD2Z4+gCJjWVJDQ3LPn2jUjFtWfuYFAAAKQ6+2w91www36/ve/v9+Y1tZWTZw4MX4djUZ10kknqa6uTvfdd99+n7344ou1YcMGRfbTibCnlaBgMMh2OACSzDa2ceP23fTU4zErQhs2sL0NAIBSk5XtcNdee60uvfTS/cYccsgh8d9v2rRJJ598sqZNm6Z777035etPmTJFy5Yt229MZWWlKisr05ovgPITiew7AZIk15Xa200c290AAChPvUqCRowYoREjRqQVG41GdfLJJ2vy5MlatGiRKipS77xbu3at/HQhBNAPtp3ZOAAAUHqyUhghGo2qrq5OY8eO1e23367/+7//i9+rqamRJD344IMaNGiQjj32WEmSZVl64IEHUm6ZA4D9SfffUfj3FgAAyldWkqBly5Zp/fr1Wr9+vQKBQMK9rkeQvvvd7+rtt9/WgAEDNHHiRD366KNqaGjIxpQAlIlQyJz5iUbN1rfuYmeCQqHczw0AABSGnPUJyhb6BAHoLlYdTkpMhDwe82tLC01PAQAoRenmBjkpkQ0AuRQOm0SntjZxPBAgAQIAADlslgoAfeU4ppqbbZuzPKFQ6vLW4bBUX9/75wAAQOkjCQJQ0CxLmjs3sex1ICA1N6de0fF6KYMNAACSsR0OQMGKne3p3vcnGjXjlpWfeQEAgOJGEgSgIDmOWQHqqXRLbKyx0cQBAAD0BkkQgIIUiSSvAHXlulJ7u4kDAADoDZIgAAXJtjMbBwAAEEMSBKAg+f2ZjQMAAIghCQJQkEIhUwUu1uC0O49HCgZNHAAAQG+QBAEoSF6vKYMtJSdCseumJvr+AACA3iMJAlCwwmGppUWqrU0cDwTMeKo+QQAAAD2hWSqAnHAcU8nNts05nlAovVWccFiqr+/bswAAAD0hCQKQdZZlev50LXkdCJjtbums5ni9Ul1d1qYHAADKDNvhAGSVZUkNDck9f6JRM25Z+ZkXAAAoXyRBALLGccwKkOsm34uNNTaaOAAAgFwhCQKQNZFI8gpQV64rtbebOAAAgFwhCQKQNbad2TgAAIBMIAkCkDV+f2bjAAAAMoEkCEDWhEKmClz3ZqcxHo8UDJo4AACAXCEJApA1Xq8pgy0lJ0Kx66Ymev4AAIDcIgkCkFXhsNTSItXWJo4HAmY8nT5BAAAAmUSzVAC94jimmpttm7M8oVDqlZxwWKqv7/1zAAAA2UASBCBtlmX6/nQtex0ImC1vqVZ0vF6pri6r0wMAAEgL2+EApMWypIaG5L4/0agZt6z8zAsAAKC3SIIApOQ4ZgXIdZPvxcYaG00cAABAoSMJApBSJJK8AtSV60rt7SYOAACg0JEEAUjJtjMbBwAAkE8kQQBS8vszGwcAAJBPJEEAUgqFTBW47g1PYzweKRg0cQAAAIWOJAhASl6vKYMtJSdCseumJvr+AACA4kASBCAt4bDU0iLV1iaOBwJmPFWfIAAAgEJBs1SgTDmOqeZm2+YsTyiUeiUnHJbq63v/HAAAQCEhCQLKkGWZvj9dy14HAmbLW6oVHa9XqqvL6vQAAACyiu1wQJmxLKmhIbnvTzRqxi0rP/MCAADIFZIgoIw4jlkBct3ke7GxxkYTBwAAUKpIgoAyEokkrwB15bpSe7uJAwAAKFUkQUAZse3MxgEAABQjkiCgjPj9mY0DAAAoRiRBQBkJhUwVuO4NT2M8HikYNHEAAACliiQIKCNerymDLSUnQrHrpib6/gAAgNJGEgSUmXBYammRamsTxwMBM56qTxAAAECxo1kqUMQcx1Rys21zjicUSm8VJxyW6uv79iwAAECxIwkCipRlmZ4/XUteBwJmu1s6qzler1RXl7XpAQAAFCy2wwFFyLKkhobknj/RqBm3rPzMCwAAoBiQBAFFxnHMCpDrJt+LjTU2mjgAAAAkIwkCikwkkrwC1JXrSu3tJg4AAADJSIKAImPbmY0DAAAoNyRBQJHx+zMbBwAAUG5IgoAiEwqZKnDdm53GeDxSMGjiAAAAkIwkCCgyXq8pgy0lJ0Kx66Ymev4AAADsC0kQUITCYamlRaqtTRwPBMx4On2CAAAAyhXNUoEC4Dimmpttm7M8oVDqlZxwWKqv7/1zAAAA5S5rK0Hjxo2Tx+NJ+Fq4cGFCzMsvv6xQKKQDDjhAwWBQt956a7amAxQsy5LGjZNOPlm68ELz67hx6TU89XqlujrpggvMryRAAAAAqWV1Jeg73/mOZs+eHb8eNmxY/PcdHR069dRTNWPGDP34xz/WK6+8oq985Suqrq7WFVdckc1pAQXDsqSGhuTGp9GoGWdrGwAAQOZlNQkaNmyYampqerz30EMPaffu3XrggQc0aNAgHXHEEVq7dq3uvPNOkiCUBceR5s5NToAkM+bxSI2NZssbKzwAAACZk9XCCAsXLtTw4cN17LHH6rbbbtPevXvj91atWqXp06dr0KBB8bGZM2dq3bp1ev/99/f5mrt27VJHR0fCF1CMIhHpnXf2fd91pfZ2EwcAAIDMydpK0Ne//nUdd9xxOuigg/THP/5R8+fPl23buvPOOyVJmzdv1vjx4xOeGTVqVPzegQce2OPrLliwQLfccku2pg3kjG1nNg4AAADp6dVK0A033JBU7KD71+uvvy5Jmjdvnurq6nT00Ufryiuv1B133KG7775bu3bt6teE58+fr+3bt8e/2tvb+/V6QL74/ZmNAwAAQHp6tRJ07bXX6tJLL91vzCGHHNLj+JQpU7R3715t3LhREyZMUE1NjbZs2ZIQE7ve1zkiSaqsrFRlZWVvpg0UpFDI9PWJRns+F+TxmPuhUO7nBgAAUMp6lQSNGDFCI0aM6NMftHbtWlVUVGjkyJGSpKlTp+rf//3ftWfPHg0cOFCStGzZMk2YMGGfW+GAUuL1Ss3Npgqcx5OYCHk85temJooiAAAAZFpWCiOsWrVKTU1N+stf/qK33npLDz30kK655hr98z//czzBufDCCzVo0CBdfvnleu211/Too4+qublZ8+bNy8aUgIIUDpsy2LW1ieOBAOWxAQAAssXjuj1txOmfF198Uf/6r/+q119/Xbt27dL48eP15S9/WfPmzUvYyvbyyy9rzpw5Wr16tQ4++GBdffXVuv7663v1Z3V0dKiqqkrbt2+Xz+fL9LcCpM1xTCU32zbneEKh9Fdx+vMsAAAAjHRzg6wkQblEEoRCYFmm50/XkteBgNnuxmoOAABAbqSbG2S1TxBQDizLnOvp3vMnGjXjlpWfeQEAAKBnJEFAPziOWQHqaT01NtbYaOIAAABQGEiCgH6IRJJXgLpyXam93cQBAACgMJAEAf1g25mNAwAAQPaRBAH94PdnNg4AAADZRxIE9EMoZKrAxZqbdufxSMGgiQMAAEBhIAkC+sHrNWWwpeREKHbd1ETPHwAAgEJCEgT0UzgstbRItbWJ44GAGadPEAAAQGEZkO8JAIXGcUw1N9s2Z3lCodQrOeGwVF/f++cAAACQeyRBQBeWZfr+dC17HQiYLW+pVnS8XqmuLqvTAwAAQAawHQ74hGVJDQ3JfX+iUTNuWfmZFwAAADKLJAiQ2QI3d65pbtpdbKyx0cQBAACguJEEATJnebqvAHXlulJ7u4kDAABAcSMJAmSKGWQyDgAAAIWLJAiQqeaWyTgAAAAULpIgQKacdSCQ3PA0xuORgkETBwAAgOJGEgTIlLdubja/754Ixa6bmuj7AwAAUApIgoBPhMNSS4tUW5s4HgiY8VR9ggAAAFAcaJaKkuU4ppqbbZuzPKFQ6pWccFiqr+/9cwAAACgeJEEoSZZl+v50LXsdCJgtb6lWdLxeqa4uq9MDAABAHrEdDiXHsqSGhuS+P9GoGbes/MwLAAAAhYEkCCXFccwKkOsm34uNNTaaOAAAAJQnkiCUlEgkeQWoK9eV2ttNHAAAAMoTSRBKim1nNg4AAAClhyQIJcXvz2wcAAAASg9JEEpKKGSqwHVveBrj8UjBoIkDAABAeSIJQknxek0ZbCk5EYpdNzXR9wcAAKCckQSh5ITDUkuLVFubOB4ImPFUfYIAAABQ2miWioLmOKaSm22bczyhUHqrOOGwVF/ft2cBAABQ2kiCULAsy/T86VryOhAw293SWc3xeqW6uqxNDwAAAEWK7XAoSJYlNTQk9/yJRs24ZeVnXgAAACh+JEEoOI5jVoBcN/lebKyx0cQBAAAAvUUShIITiSSvAHXlulJ7u4kDAAAAeoskCAXHtjMbBwAAAHRFEoSC4/dnNg4AAADoiiQIBScUMlXgujc7jfF4pGDQxAEAAAC9RRKEguP1mjLYUnIiFLtuaqLnDwAAAPqGJAgFKRyWWlqk2trE8UDAjKfTJwgAAADoCc1SkROOY6q52bY5yxMKpV7JCYel+vrePwcAAADsD0kQss6yTN+frmWvAwGz5S3Vio7XK9XVZXV6AAAAKDNsh0NWWZbU0JDc9ycaNeOWlZ95AQAAoHyRBCFrHMesALlu8r3YWGOjiQMAAAByhSQIWROJJK8AdeW6Unu7iQMAAAByhSQIWWPbmY0DAAAAMoEkCFnj92c2DgAAAMgEkiBkTShkqsB1b3ga4/FIwaCJAwAAAHKFJAhZ4/WaMthSciIUu25qou8PAAAAcoskCFkVDkstLVJtbeJ4IGDGU/UJAgAAADKNZqnoFccx1dxs25zlCYVSr+SEw1J9fe+fAwAAALKBJAhpsyzT96dr2etAwGx5S7Wi4/VKdXVZnR4AAACQFrbDIS2WJTU0JPf9iUbNuGXlZ14AAABAb2UlCVq5cqU8Hk+PX6tXr5Ykbdy4scf7zz//fDamhH5wHLMC5LrJ92JjjY0mDgAAACh0WdkON23aNNndOmB++9vf1ooVK3T88ccnjC9fvlxHHHFE/Hr48OHZmBL6IRJJXgHqynWl9nYTx5Y3AAAAFLqsJEGDBg1STU1N/HrPnj369a9/rauvvlqebrWShw8fnhCLwtMtn+13HAAAAJBPOTkT9MQTT+jdd9/VZZddlnTvrLPO0siRI3XiiSfqiSeeSPlau3btUkdHR8IXssvvz2wcAAAAkE85SYLuv/9+zZw5U4FAID42dOhQ3XHHHXrsscf0P//zPzrxxBN19tlnp0yEFixYoKqqqvhXMBjM9vTLXihkqsB1b3ga4/FIwaCJAwAAAAqdx3V7Ou7esxtuuEHf//739xvT2tqqiRMnxq/feecdjR07VkuWLNG5556732cvvvhibdiwQZFIZJ8xu3bt0q5du+LXHR0dCgaD2r59u3w+X5rfCXorVh1OSiyQEEuMaHwKAACAfOvo6FBVVVXK3KBXZ4KuvfZaXXrppfuNOeSQQxKuFy1apOHDh+uss85K+fpTpkzRsmXL9htTWVmpysrKlK+FzAqHTaLTU5+gpiYSIAAAABSPXiVBI0aM0IgRI9KOd11XixYt0sUXX6yBAwemjF+7dq38HCzJOscxldxs25zjCYVMM9NUwmGpvr5vzwIAAACFIivV4WKeeuopbdiwQV/96leT7j344IMaNGiQjj32WEmSZVl64IEHdN9992VzSmXPsnpezWluTm81x+ulDDYAAACKW1aToPvvv1/Tpk1LOCPU1Xe/+129/fbbGjBggCZOnKhHH31UDbGDJ8i42Lme7qfAolEzzrkeAAAAlINeFUYoROkefip3jiONG7fvpqcej1kR2rCB7W0AAAAoTunmBjkpkY38i0T2nQBJZnWovd3EAQAAAKWMJKhM2HZm4wAAAIBiRRJUJtItukdxPgAAAJQ6kqAyEQqZMz+x5qbdeTxSMGjiAAAAgFJGElQmvF5TBltKToRi101NFEUAAABA6SMJKiPhsCmDXVubOB4IUB4bAAAA5SOrfYKQXY5jqrnZtjnLEwqlXskJh6X6+t4/BwAAAJQKkqAiZVnS3LmJZa8DAbPlLdWKjtcr1dVldXoAAABAwWI7XBGyLKmhIbnvTzRqxi0rP/MCAAAAigFJUJFxHLMC5LrJ92JjjY0mDgAAAEAykqAiE4kkrwB15bpSe7uJAwAAAJCMJKjI2HZm4wAAAIByQxJUZPz+zMYBAAAA5YYkqMiEQqYKXPeGpzEejxQMmjgAAAAAyUiCiozXa8pgS8mJUOy6qYm+PwAAAMC+kAQVoXBYammRamsTxwMBM56qTxAAAABQzmiWmmeOYyq52bY5xxMKpbeKEw5L9fV9exYAAAAoZyRBeWRZpudP15LXgYDZ7pbOao7XK9XVZW16AAAAQEliO1yeWJbU0JDc8ycaNeOWlZ95AQAAAKWOJCgPHMesALlu8r3YWGOjiQMAAACQWSRBeRCJJK8AdeW6Unu7iQMAAACQWSRBeWDbmY0DAAAAkD6SoDzw+zMbBwAAACB9JEF5EAqZKnDdm53GeDxSMGjiAAAAAGQWSVAeeL2mDLaUnAjFrpua6PkDAAAAZANJUJ6Ew1JLi1RbmzgeCJjxdPoEAQAAAOg9mqVmiOOYam62bc7yhEKpV3LCYam+vvfPAQAAAOg7kqAMsCzT96dr2etAwGx5S7Wi4/VKdXVZnR4AAACALtgO10+WJTU0JPf9iUbNuGXlZ14AAAAAekYS1A+OY1aAXDf5XmyssdHEAQAAACgMJEH9EIkkrwB15bpSe7uJAwAAAFAYSIL6wbYzGwcAAAAg+0iC+sHvz2wcAAAAgOwjCeqHUMhUgeve8DTG45GCQRMHAAAAoDCQBPWD12vKYEvJiVDsuqmJvj8AAABAISEJ6qdwWGppkWprE8cDATOeqk8QAAAAgNyiWWoGhMNSfb2pAmfb5gxQKMQKEAAAAFCISIIyxOuV6uryPQsAAAAAqbAdDgAAAEBZIQkCAAAAUFZIggAAAACUFZIgAAAAAGWFJAgAAABAWSEJAgAAAFBWSIIAAAAAlBWSIAAAAABlhSQIAAAAQFkhCQIAAABQVkiCAAAAAJQVkiAAAAAAZYUkCAAAAEBZIQkCAAAAUFZIggAAAACUFZIgAAAAAGVlQL4n0F+u60qSOjo68jwTAAAAAPkUywliOcK+FH0StGPHDklSMBjM80wAAAAAFIIdO3aoqqpqn/c9bqo0qcB1dnZq06ZNGjZsmDweT17n0tHRoWAwqPb2dvl8vrzOpZTxPucG73Nu8D7nBu9z9vEe5wbvc27wPudGNt5n13W1Y8cOjR49WhUV+z75U/QrQRUVFQoEAvmeRgKfz8f/YHKA9zk3eJ9zg/c5N3ifs4/3ODd4n3OD9zk3Mv0+728FKIbCCAAAAADKCkkQAAAAgLJCEpRBlZWVuummm1RZWZnvqZQ03ufc4H3ODd7n3OB9zj7e49zgfc4N3ufcyOf7XPSFEQAAAACgN1gJAgAAAFBWSIIAAAAAlBWSIAAAAABlhSQIAAAAQFkhCQIAAABQVkiC+uh73/uepk2bpiFDhqi6urrHmLa2Np1xxhkaMmSIRo4cqW984xvau3dvQszKlSt13HHHqbKyUocddpgWL16c/ckXqZUrV8rj8fT4tXr1aknSxo0be7z//PPP53n2xWXcuHFJ7+HChQsTYl5++WWFQiEdcMABCgaDuvXWW/M02+K0ceNGXX755Ro/frwGDx6sQw89VDfddJN2796dEMPnuf/uuecejRs3TgcccICmTJmiP/3pT/meUlFbsGCBTjjhBA0bNkwjR47U2WefrXXr1iXE1NXVJX1ur7zyyjzNuDjdfPPNSe/hxIkT4/c//vhjzZkzR8OHD9fQoUN17rnnasuWLXmccXHq6f/vPB6P5syZI4nPcl/84Q9/0JlnnqnRo0fL4/Ho8ccfT7jvuq5uvPFG+f1+DR48WDNmzNAbb7yREPPee+/poosuks/nU3V1tS6//HLt3Lkzo/MkCeqj3bt364tf/KK+9rWv9XjfcRydccYZ2r17t/74xz/qwQcf1OLFi3XjjTfGYzZs2KAzzjhDJ598stauXavGxkZ99atf1e9///tcfRtFZdq0abJtO+Hrq1/9qsaPH6/jjz8+IXb58uUJcZMnT87TrIvXd77znYT38Oqrr47f6+jo0KmnnqqxY8dqzZo1uu2223TzzTfr3nvvzeOMi8vrr7+uzs5O/eQnP9Frr72mH/zgB/rxj3+sf/u3f0uK5fPcd48++qjmzZunm266SS+++KI++9nPaubMmdq6dWu+p1a0nnnmGc2ZM0fPP/+8li1bpj179ujUU0/VBx98kBA3e/bshM8t/1DSe0cccUTCe/jss8/G711zzTX6zW9+o8cee0zPPPOMNm3apHA4nMfZFqfVq1cnvMfLli2TJH3xi1+Mx/BZ7p0PPvhAn/3sZ3XPPff0eP/WW2/VXXfdpR//+Md64YUX9KlPfUozZ87Uxx9/HI+56KKL9Nprr2nZsmX67W9/qz/84Q+64oorMjtRF/2yaNEit6qqKmn8d7/7nVtRUeFu3rw5PvajH/3I9fl87q5du1zXdd1vfvOb7hFHHJHw3Pnnn+/OnDkzq3MuFbt373ZHjBjhfuc734mPbdiwwZXkvvTSS/mbWAkYO3as+4Mf/GCf93/4wx+6Bx54YPyz7Lque/3117sTJkzIwexK16233uqOHz8+fs3nuf8+97nPuXPmzIlfO47jjh492l2wYEEeZ1Vatm7d6kpyn3nmmfjYSSed5M6dOzd/kyoBN910k/vZz362x3vbtm1zBw4c6D722GPxsdbWVleSu2rVqhzNsDTNnTvXPfTQQ93Ozk7Xdfks95ck91e/+lX8urOz062pqXFvu+22+Ni2bdvcyspK9xe/+IXruq7717/+1ZXkrl69Oh7z5JNPuh6Px41GoxmbGytBWbJq1SodddRRGjVqVHxs5syZ6ujo0GuvvRaPmTFjRsJzM2fO1KpVq3I612L1xBNP6N1339Vll12WdO+ss87SyJEjdeKJJ+qJJ57Iw+yK38KFCzV8+HAde+yxuu222xK2cq5atUrTp0/XoEGD4mMzZ87UunXr9P777+djuiVh+/btOuigg5LG+Tz3ze7du7VmzZqEv2crKio0Y8YM/p7NoO3bt0tS0mf3oYce0sEHH6wjjzxS8+fP14cffpiP6RW1N954Q6NHj9Yhhxyiiy66SG1tbZKkNWvWaM+ePQmf7YkTJ2rMmDF8tvth9+7d+vnPf66vfOUr8ng88XE+y5mzYcMGbd68OeGzW1VVpSlTpsQ/u6tWrVJ1dXXCLp8ZM2aooqJCL7zwQsbmMiBjr4QEmzdvTkiAJMWvN2/evN+Yjo4OffTRRxo8eHBuJluk7r//fs2cOVOBQCA+NnToUN1xxx36/Oc/r4qKCv3yl7/U2Wefrccff1xnnXVWHmdbXL7+9a/ruOOO00EHHaQ//vGPmj9/vmzb1p133inJfHbHjx+f8EzXz/eBBx6Y8zkXu/Xr1+vuu+/W7bffHh/j89w/f//73+U4To9/z77++ut5mlVp6ezsVGNjoz7/+c/ryCOPjI9feOGFGjt2rEaPHq2XX35Z119/vdatWyfLsvI42+IyZcoULV68WBMmTJBt27rlllsUCoX06quvavPmzRo0aFDSmeRRo0bFf8ZA7z3++OPatm2bLr300vgYn+XMin0+e/p7uevPxyNHjky4P2DAAB100EEZ/XyTBHVxww036Pvf//5+Y1pbWxMOJqL/+vK+v/POO/r973+vJUuWJMQdfPDBmjdvXvz6hBNO0KZNm3TbbbeV/Q+NvXmfu76HRx99tAYNGqR/+Zd/0YIFC1RZWZntqRa1vnyeo9GoZs2apS9+8YuaPXt2fJzPMwrdnDlz9OqrryacVZGUsHf/qKOOkt/v1ymnnKI333xThx56aK6nWZROO+20+O+PPvpoTZkyRWPHjtWSJUv4R9Isuf/++3Xaaadp9OjR8TE+y6WLJKiLa6+9NiH778khhxyS1mvV1NQkVSCKVW2pqamJ/9q9ksuWLVvk8/nK6i+4vrzvixYt0vDhw9P6QXDKlCnxg47lrD+f7ylTpmjv3r3auHGjJkyYsM/PrvSPz3e56u37vGnTJp188smaNm1aWoUl+Dyn7+CDD5bX6+3xs1run9NMuOqqq+IHlruuyPdkypQpksyKJz849k11dbU+/elPa/369frCF76g3bt3a9u2bQmrQXy2++7tt9/W8uXLU67w8Fnun9jnc8uWLfL7/fHxLVu26JhjjonHdC9es3fvXr333nsZ/XyTBHUxYsQIjRgxIiOvNXXqVH3ve9/T1q1b40t6y5Ytk8/n02c+85l4zO9+97uE55YtW6apU6dmZA7Forfvu+u6WrRokS6++GINHDgwZfzatWsT/odWrvrz+V67dq0qKirin+WpU6fq3//937Vnz574f4Nly5ZpwoQJZb8VrjfvczQa1cknn6zJkydr0aJFqqhIfUyTz3P6Bg0apMmTJ2vFihU6++yzJZntWytWrNBVV12V38kVMdd1dfXVV+tXv/qVVq5cmbQ1tidr166VJD67/bBz5069+eab+vKXv6zJkydr4MCBWrFihc4991xJ0rp169TW1lZ2P0NkyqJFizRy5EidccYZ+43js9w/48ePV01NjVasWBFPejo6OvTCCy/EKy5PnTpV27Zt05o1a+LVUJ966il1dnbGk9CMyFiJhTLz9ttvuy+99JJ7yy23uEOHDnVfeukl96WXXnJ37Njhuq7r7t271z3yyCPdU0891V27dq27dOlSd8SIEe78+fPjr/HWW2+5Q4YMcb/xjW+4ra2t7j333ON6vV536dKl+fq2isLy5ctdSW5ra2vSvcWLF7sPP/yw29ra6ra2trrf+9733IqKCveBBx7Iw0yL0x//+Ef3Bz/4gbt27Vr3zTffdH/+85+7I0aMcC+++OJ4zLZt29xRo0a5X/7yl91XX33VfeSRR9whQ4a4P/nJT/I48+LyzjvvuIcddph7yimnuO+8845r23b8K4bPc/898sgjbmVlpbt48WL3r3/9q3vFFVe41dXVCZU70Ttf+9rX3KqqKnflypUJn9sPP/zQdV3XXb9+vfud73zH/fOf/+xu2LDB/fWvf+0ecsgh7vTp0/M88+Jy7bXXuitXrnQ3bNjgPvfcc+6MGTPcgw8+2N26davruq575ZVXumPGjHGfeuop989//rM7depUd+rUqXmedXFyHMcdM2aMe/311yeM81numx07dsR/Lpbk3nnnne5LL73kvv32267ruu7ChQvd6upq99e//rX78ssvu/X19e748ePdjz76KP4as2bNco899lj3hRdecJ999ln38MMPdy+44IKMzpMkqI8uueQSV1LS19NPPx2P2bhxo3vaaae5gwcPdg8++GD32muvdffs2ZPwOk8//bR7zDHHuIMGDXIPOeQQd9GiRbn9RorQBRdc4E6bNq3He4sXL3YnTZrkDhkyxPX5fO7nPve5hBKiSG3NmjXulClT3KqqKveAAw5wJ02a5P7nf/6n+/HHHyfE/eUvf3FPPPFEt7Ky0q2trXUXLlyYpxkXp0WLFvX4d0jXf5vi85wZd999tztmzBh30KBB7uc+9zn3+eefz/eUitq+Prex//9qa2tzp0+f7h500EFuZWWle9hhh7nf+MY33O3bt+d34kXm/PPPd/1+vzto0CC3trbWPf/8893169fH73/00Ufuv/7rv7oHHnigO2TIEPecc85J+EcUpO/3v/+9K8ldt25dwjif5b55+umne/w74pJLLnFd15TJ/va3v+2OGjXKraysdE855ZSk9/7dd991L7jgAnfo0KGuz+dzL7vssvhCQ6Z4XNd1M7euBAAAAACFjT5BAAAAAMoKSRAAAACAskISBAAAAKCskAQBAAAAKCskQQAAAADKCkkQAAAAgLJCEgQAAACgrJAEAQAAACgrJEEAAAAAygpJEAAAAICyQhIEAAAAoKz8/0LCCyRNM/qNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "#plot taining data in blue\n",
    "plt.scatter(X_train, y_train, c=\"b\", label=\"Training Data\")\n",
    "#plot Test data in green\n",
    "plt.scatter(X_test, y_test, c=\"g\", label=\"Testing Data\")\n",
    "#show a legend\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fb381646",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's have a look at how to build a neural network for our data\n",
    "\n",
    "# 1. Create a model\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "#2. Compile the model\n",
    "\n",
    "model.compile(loss=tf.keras.losses.mae,\n",
    "              optimizer=tf.keras.optimizers.SGD(),\n",
    "              metrics=[\"mae\"])\n",
    "\n",
    "# 3. fit the model\n",
    "#model.fit(X_train, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e31903c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualizing the Model\n",
    "\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d86bcf47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets a build a model that builds automatically defining the input_shape\n",
    "\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(1, input_shape = [1])\n",
    "])\n",
    "\n",
    "model.compile(loss=tf.keras.losses.mae,\n",
    "             optimizer=tf.keras.optimizers.SGD(),\n",
    "             metrics=[\"mae\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "03ecbf60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_7 (Dense)             (None, 1)                 2         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7acb9b9",
   "metadata": {},
   "source": [
    "Total params - total number of parameters in the model\n",
    "Trainable params - these are the parameters, patterns the model can update as it trains\n",
    "Non Trainable params - these parameters arent update during training. This is typical already trained parameters\n",
    "\n",
    "📖 **Resourse:** For more indepth information about trainable parameters within a layer, check out MIT Introduction to Deep Learning Video\n",
    "introtodeeplearning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "59329630",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(64, input_shape = [1], name=\"input_layer\"),\n",
    "    tf.keras.layers.Dense(32, activation='relu'),\n",
    "    tf.keras.layers.Dense(1, name=\"output_layer\"),\n",
    "], name=\"Model_1\")\n",
    "\n",
    "model.compile(loss=tf.keras.losses.mae,\n",
    "             optimizer=tf.keras.optimizers.Adam(),\n",
    "             metrics=[\"mae\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ffed2b1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (Dense)         (None, 64)                128       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,241\n",
      "Trainable params: 2,241\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fb35fb25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2/2 [==============================] - 2s 8ms/step - loss: 38.1024 - mae: 38.1024\n",
      "Epoch 2/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 34.4813 - mae: 34.4813\n",
      "Epoch 3/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 31.2749 - mae: 31.2749\n",
      "Epoch 4/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 28.0964 - mae: 28.0964\n",
      "Epoch 5/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 25.0557 - mae: 25.0557\n",
      "Epoch 6/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 22.0676 - mae: 22.0676\n",
      "Epoch 7/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 18.7803 - mae: 18.7803\n",
      "Epoch 8/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 15.3848 - mae: 15.3848\n",
      "Epoch 9/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 12.6588 - mae: 12.6588\n",
      "Epoch 10/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 11.3596 - mae: 11.3596\n",
      "Epoch 11/100\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 12.5849 - mae: 12.5849\n",
      "Epoch 12/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 12.2086 - mae: 12.2086\n",
      "Epoch 13/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 10.7047 - mae: 10.7047\n",
      "Epoch 14/100\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 8.6213 - mae: 8.6213\n",
      "Epoch 15/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.5539 - mae: 7.5539\n",
      "Epoch 16/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.3477 - mae: 7.3477\n",
      "Epoch 17/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.6996 - mae: 6.6996\n",
      "Epoch 18/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 6.0527 - mae: 6.0527\n",
      "Epoch 19/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 4.9885 - mae: 4.9885\n",
      "Epoch 20/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 4.5293 - mae: 4.5293\n",
      "Epoch 21/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.4994 - mae: 4.4994\n",
      "Epoch 22/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 4.6245 - mae: 4.6245\n",
      "Epoch 23/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 5.0041 - mae: 5.0041\n",
      "Epoch 24/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 5.1284 - mae: 5.1284\n",
      "Epoch 25/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.8209 - mae: 4.8209\n",
      "Epoch 26/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.5438 - mae: 4.5438\n",
      "Epoch 27/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.3708 - mae: 4.3708\n",
      "Epoch 28/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.2372 - mae: 4.2372\n",
      "Epoch 29/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.2013 - mae: 4.2013\n",
      "Epoch 30/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.1679 - mae: 4.1679\n",
      "Epoch 31/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.1785 - mae: 4.1785\n",
      "Epoch 32/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.1607 - mae: 4.1607\n",
      "Epoch 33/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.1552 - mae: 4.1552\n",
      "Epoch 34/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.1403 - mae: 4.1403\n",
      "Epoch 35/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.0355 - mae: 4.0355\n",
      "Epoch 36/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9996 - mae: 3.9996\n",
      "Epoch 37/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.0695 - mae: 4.0695\n",
      "Epoch 38/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.1347 - mae: 4.1347\n",
      "Epoch 39/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.1374 - mae: 4.1374\n",
      "Epoch 40/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.0873 - mae: 4.0873\n",
      "Epoch 41/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.0105 - mae: 4.0105\n",
      "Epoch 42/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9718 - mae: 3.9718\n",
      "Epoch 43/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 3.9842 - mae: 3.9842\n",
      "Epoch 44/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.0085 - mae: 4.0085\n",
      "Epoch 45/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 3.9450 - mae: 3.9450\n",
      "Epoch 46/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9399 - mae: 3.9399\n",
      "Epoch 47/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 3.9763 - mae: 3.9763\n",
      "Epoch 48/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 4.0117 - mae: 4.0117\n",
      "Epoch 49/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9983 - mae: 3.9983\n",
      "Epoch 50/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 3.9752 - mae: 3.9752\n",
      "Epoch 51/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9527 - mae: 3.9527\n",
      "Epoch 52/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9592 - mae: 3.9592\n",
      "Epoch 53/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 3.9019 - mae: 3.9019\n",
      "Epoch 54/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8877 - mae: 3.8877\n",
      "Epoch 55/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8726 - mae: 3.8726\n",
      "Epoch 56/100\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 3.9183 - mae: 3.9183\n",
      "Epoch 57/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9794 - mae: 3.9794\n",
      "Epoch 58/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.0390 - mae: 4.0390\n",
      "Epoch 59/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9609 - mae: 3.9609\n",
      "Epoch 60/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9127 - mae: 3.9127\n",
      "Epoch 61/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9294 - mae: 3.9294\n",
      "Epoch 62/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9716 - mae: 3.9716\n",
      "Epoch 63/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 3.9488 - mae: 3.9488\n",
      "Epoch 64/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8664 - mae: 3.8664\n",
      "Epoch 65/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8197 - mae: 3.8197\n",
      "Epoch 66/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9180 - mae: 3.9180\n",
      "Epoch 67/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 3.9095 - mae: 3.9095\n",
      "Epoch 68/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8241 - mae: 3.8241\n",
      "Epoch 69/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8058 - mae: 3.8058\n",
      "Epoch 70/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8601 - mae: 3.8601\n",
      "Epoch 71/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8693 - mae: 3.8693\n",
      "Epoch 72/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8402 - mae: 3.8402\n",
      "Epoch 73/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.7613 - mae: 3.7613\n",
      "Epoch 74/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 3.7379 - mae: 3.7379\n",
      "Epoch 75/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8251 - mae: 3.8251\n",
      "Epoch 76/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9690 - mae: 3.9690\n",
      "Epoch 77/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.9493 - mae: 3.9493\n",
      "Epoch 78/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 3.7785 - mae: 3.7785\n",
      "Epoch 79/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.7203 - mae: 3.7203\n",
      "Epoch 80/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 3.8240 - mae: 3.8240\n",
      "Epoch 81/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 3.8106 - mae: 3.8106\n",
      "Epoch 82/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.7126 - mae: 3.7126\n",
      "Epoch 83/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.7015 - mae: 3.7015\n",
      "Epoch 84/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8238 - mae: 3.8238\n",
      "Epoch 85/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.8199 - mae: 3.8199\n",
      "Epoch 86/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 3.6749 - mae: 3.6749\n",
      "Epoch 87/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 3.6199 - mae: 3.6199\n",
      "Epoch 88/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.2338 - mae: 4.2338\n",
      "Epoch 89/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.3173 - mae: 4.3173\n",
      "Epoch 90/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 3.7343 - mae: 3.7343\n",
      "Epoch 91/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.7245 - mae: 3.7245\n",
      "Epoch 92/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.0442 - mae: 4.0442\n",
      "Epoch 93/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.1433 - mae: 4.1433\n",
      "Epoch 94/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 4.0028 - mae: 4.0028\n",
      "Epoch 95/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 3.8022 - mae: 3.8022\n",
      "Epoch 96/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 3.5953 - mae: 3.5953\n",
      "Epoch 97/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 3.7213 - mae: 3.7213\n",
      "Epoch 98/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.7742 - mae: 3.7742\n",
      "Epoch 99/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 3.6036 - mae: 3.6036\n",
      "Epoch 100/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 3.6047 - mae: 3.6047\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x14eba430a30>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=100, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "336884a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (Dense)         (None, 64)                128       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,241\n",
      "Trainable params: 2,241\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Get a summary of a model\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e166e8ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "plot_model(model=model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d85827f5",
   "metadata": {},
   "source": [
    "### To visualize predictions\n",
    "\n",
    "To visualize predictions it's a good idea to plot them agains the ground of truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2c3dfff6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 77ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 73.62869 ],\n",
       "       [ 78.42958 ],\n",
       "       [ 83.230484],\n",
       "       [ 88.03139 ],\n",
       "       [ 92.832275],\n",
       "       [ 97.63319 ],\n",
       "       [102.43408 ],\n",
       "       [107.234985],\n",
       "       [112.03587 ],\n",
       "       [116.83678 ]], dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make some predictions\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b50f0bfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106])>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "429fd328",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets create a plotting function\n",
    "\n",
    "def plot_prediction(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",
    "    plt.scatter(train_data, train_labels, c='b', label='Training Data')\n",
    "    plt.scatter(test_data, test_labels, c='g', label=\"Testing Data\")\n",
    "    plt.scatter(test_data, predictions, c='r', label=\"Predictions\")\n",
    "    plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eea8532e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f84f6f",
   "metadata": {},
   "source": [
    "### Evaluating our model with an evaluation metrics\n",
    "\n",
    "Depending on the problem you are working on, there will be different evaluation metrics to evaluate your model performance\n",
    "\n",
    "Since we are working with regression two of the main metrics:\n",
    "\n",
    "* MAE - Mean Absolute Error, \"On Average how wrong is each of my model's prediction\n",
    "\n",
    "* MSE - Mean Squared Error, \"Square the Average errors\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2cba768f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 347ms/step - loss: 6.6662 - mae: 6.6662\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[6.6661696434021, 6.6661696434021]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "\n",
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b2951655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 73.1731  ],\n",
       "       [ 77.94934 ],\n",
       "       [ 82.72558 ],\n",
       "       [ 87.501816],\n",
       "       [ 92.278046],\n",
       "       [ 97.05429 ],\n",
       "       [101.83052 ],\n",
       "       [106.606766],\n",
       "       [111.382996],\n",
       "       [116.15924 ]], dtype=float32)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "47e67f6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 70,  74,  78,  82,  86,  90,  94,  98, 102, 106])>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3cce7a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
       "array([15.461517, 12.430395, 10.654884, 10.      , 10.455609, 12.021716,\n",
       "       14.698313, 18.606766, 23.382996, 28.15924 ], dtype=float32)>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the mean absolute error\n",
    "\n",
    "mae = tf.metrics.mean_absolute_error(y_test, y_pred)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "64808ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[ -3,   1,   5,   9,  13,  17,  21,  25,  29,  33],\n",
       "       [ -7,  -3,   1,   5,   9,  13,  17,  21,  25,  29],\n",
       "       [-12,  -8,  -4,   0,   4,   8,  12,  16,  20,  24],\n",
       "       [-17, -13,  -9,  -5,  -1,   3,   7,  11,  15,  19],\n",
       "       [-22, -18, -14, -10,  -6,  -2,   2,   6,  10,  14],\n",
       "       [-27, -23, -19, -15, -11,  -7,  -3,   1,   5,   9],\n",
       "       [-31, -27, -23, -19, -15, -11,  -7,  -3,   1,   5],\n",
       "       [-36, -32, -28, -24, -20, -16, -12,  -8,  -4,   0],\n",
       "       [-41, -37, -33, -29, -25, -21, -17, -13,  -9,  -5],\n",
       "       [-46, -42, -38, -34, -30, -26, -22, -18, -14, -10]])>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test - y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563f5f45",
   "metadata": {},
   "source": [
    "### Running experiments to improve our model\n",
    "\n",
    "To improve your model\n",
    "1. Get more Data\n",
    "2. Make your model more larger or complex\n",
    "3. Train for longer - more iterations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0982cdc",
   "metadata": {},
   "source": [
    "### Build `model_1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0d35527c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(40,), dtype=int32, numpy=\n",
       "array([-100,  -96,  -92,  -88,  -84,  -80,  -76,  -72,  -68,  -64,  -60,\n",
       "        -56,  -52,  -48,  -44,  -40,  -36,  -32,  -28,  -24,  -20,  -16,\n",
       "        -12,   -8,   -4,    0,    4,    8,   12,   16,   20,   24,   28,\n",
       "         32,   36,   40,   44,   48,   52,   56])>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c732ce93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(40,), dtype=int32, numpy=\n",
       "array([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42,\n",
       "       -38, -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,\n",
       "        14,  18,  22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,\n",
       "        66])>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e700ea6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 31.7166 - mae: 31.7166\n",
      "Epoch 2/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.9053 - mae: 8.9053\n",
      "Epoch 3/100\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 9.7724 - mae: 9.7724\n",
      "Epoch 4/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.9482 - mae: 11.9482\n",
      "Epoch 5/100\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 11.0125 - mae: 11.0125\n",
      "Epoch 6/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 10.1728 - mae: 10.1728\n",
      "Epoch 7/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.1904 - mae: 9.1904\n",
      "Epoch 8/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.1270 - mae: 9.1270\n",
      "Epoch 9/100\n",
      "2/2 [==============================] - 0s 9ms/step - loss: 11.7363 - mae: 11.7363\n",
      "Epoch 10/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.6544 - mae: 13.6544\n",
      "Epoch 11/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 11.7192 - mae: 11.7192\n",
      "Epoch 12/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 16.4588 - mae: 16.4588\n",
      "Epoch 13/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.9775 - mae: 11.9775\n",
      "Epoch 14/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 13.9210 - mae: 13.9210\n",
      "Epoch 15/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 11.3106 - mae: 11.3106\n",
      "Epoch 16/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.5611 - mae: 8.5611\n",
      "Epoch 17/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.6916 - mae: 13.6916\n",
      "Epoch 18/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.5355 - mae: 11.5355\n",
      "Epoch 19/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 17.8157 - mae: 17.8157\n",
      "Epoch 20/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 14.9510 - mae: 14.9510\n",
      "Epoch 21/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.8326 - mae: 10.8326\n",
      "Epoch 22/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.5824 - mae: 8.5824\n",
      "Epoch 23/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.7091 - mae: 9.7091\n",
      "Epoch 24/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.9450 - mae: 10.9450\n",
      "Epoch 25/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 9.1510 - mae: 9.1510\n",
      "Epoch 26/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.1828 - mae: 13.1828\n",
      "Epoch 27/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.6548 - mae: 10.6548\n",
      "Epoch 28/100\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 12.8735 - mae: 12.8735\n",
      "Epoch 29/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 9.5013 - mae: 9.5013\n",
      "Epoch 30/100\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 16.3963 - mae: 16.3963\n",
      "Epoch 31/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 23.5868 - mae: 23.5868\n",
      "Epoch 32/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.6035 - mae: 7.6035\n",
      "Epoch 33/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.3014 - mae: 9.3014\n",
      "Epoch 34/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.6831 - mae: 13.6831\n",
      "Epoch 35/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.1430 - mae: 11.1430\n",
      "Epoch 36/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.3629 - mae: 13.3629\n",
      "Epoch 37/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.4491 - mae: 9.4491\n",
      "Epoch 38/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.1025 - mae: 10.1025\n",
      "Epoch 39/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.1949 - mae: 10.1949\n",
      "Epoch 40/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.9390 - mae: 10.9390\n",
      "Epoch 41/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 7.9143 - mae: 7.9143\n",
      "Epoch 42/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.5805 - mae: 10.5805\n",
      "Epoch 43/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.2022 - mae: 7.2022\n",
      "Epoch 44/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.9926 - mae: 7.9926\n",
      "Epoch 45/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.7866 - mae: 9.7866\n",
      "Epoch 46/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.8635 - mae: 8.8635\n",
      "Epoch 47/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 7.5637 - mae: 7.5637\n",
      "Epoch 48/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.5608 - mae: 8.5608\n",
      "Epoch 49/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.9926 - mae: 9.9926\n",
      "Epoch 50/100\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 9.0121 - mae: 9.0121\n",
      "Epoch 51/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.6631 - mae: 10.6631\n",
      "Epoch 52/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 15.2859 - mae: 15.2859\n",
      "Epoch 53/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 14.3086 - mae: 14.3086\n",
      "Epoch 54/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 21.6011 - mae: 21.6011\n",
      "Epoch 55/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 15.9929 - mae: 15.9929\n",
      "Epoch 56/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.2776 - mae: 10.2776\n",
      "Epoch 57/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.7619 - mae: 9.7619\n",
      "Epoch 58/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 9.0471 - mae: 9.0471\n",
      "Epoch 59/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.2528 - mae: 8.2528\n",
      "Epoch 60/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 9.3484 - mae: 9.3484\n",
      "Epoch 61/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.1592 - mae: 11.1592\n",
      "Epoch 62/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.0660 - mae: 12.0660\n",
      "Epoch 63/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.2615 - mae: 7.2615\n",
      "Epoch 64/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.4275 - mae: 12.4275\n",
      "Epoch 65/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.4951 - mae: 10.4951\n",
      "Epoch 66/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 15.5993 - mae: 15.5993\n",
      "Epoch 67/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.0008 - mae: 10.0008\n",
      "Epoch 68/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.7120 - mae: 8.7120\n",
      "Epoch 69/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.4754 - mae: 13.4754\n",
      "Epoch 70/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.4598 - mae: 7.4598\n",
      "Epoch 71/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.2220 - mae: 12.2220\n",
      "Epoch 72/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 8.5218 - mae: 8.5218\n",
      "Epoch 73/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 7.0301 - mae: 7.0301\n",
      "Epoch 74/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.9180 - mae: 9.9180\n",
      "Epoch 75/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.9311 - mae: 9.9311\n",
      "Epoch 76/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.0969 - mae: 10.0969\n",
      "Epoch 77/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 12.9502 - mae: 12.9502\n",
      "Epoch 78/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.1385 - mae: 11.1385\n",
      "Epoch 79/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 14.6897 - mae: 14.6897\n",
      "Epoch 80/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.9190 - mae: 8.9190\n",
      "Epoch 81/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.7636 - mae: 10.7636\n",
      "Epoch 82/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.3768 - mae: 8.3768\n",
      "Epoch 83/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.2194 - mae: 9.2194\n",
      "Epoch 84/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.9349 - mae: 8.9349\n",
      "Epoch 85/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 13.2006 - mae: 13.2006\n",
      "Epoch 86/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.6943 - mae: 13.6943\n",
      "Epoch 87/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.1783 - mae: 13.1783\n",
      "Epoch 88/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 4ms/step - loss: 11.5031 - mae: 11.5031\n",
      "Epoch 89/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 7.7937 - mae: 7.7937\n",
      "Epoch 90/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.9172 - mae: 10.9172\n",
      "Epoch 91/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 6.7407 - mae: 6.7407\n",
      "Epoch 92/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.1096 - mae: 10.1096\n",
      "Epoch 93/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.6014 - mae: 7.6014\n",
      "Epoch 94/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.2336 - mae: 9.2336\n",
      "Epoch 95/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 10.8251 - mae: 10.8251\n",
      "Epoch 96/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.2834 - mae: 10.2834\n",
      "Epoch 97/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.6741 - mae: 7.6741\n",
      "Epoch 98/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 8.6087 - mae: 8.6087\n",
      "Epoch 99/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.3873 - mae: 9.3873\n",
      "Epoch 100/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 8.8324 - mae: 8.8324\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x14eba14c8b0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model_1 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(1, input_shape = [1], name=\"input_layer\")\n",
    "])\n",
    "\n",
    "model_1.compile(loss=tf.keras.losses.mae,\n",
    "               optimizer=tf.keras.optimizers.SGD(),\n",
    "               metrics=[\"mae\"])\n",
    "\n",
    "model_1.fit(X_train, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "031120b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (Dense)         (None, 1)                 2         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a9239ed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 19ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make and plot for model_1\n",
    "\n",
    "y_pred_1 = model_1.predict(X_test)\n",
    "plot_prediction(predictions=y_pred_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1db83677",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make some function to calculate mae and mse to be reutilize later\n",
    "\n",
    "def mae(y_true, y_pred):\n",
    "    return tf.metrics.mean_absolute_error(y_true= y_true,\n",
    "                                         y_pred=tf.squeeze(y_pred))\n",
    "\n",
    "def mse(y_true, y_pred):\n",
    "    return tf.metrics.mean_squared_error(y_true=y_true,\n",
    "                                        y_pred=tf.squeeze(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9ac8fc54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(), dtype=float32, numpy=8.666227>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=81.63689>)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae_1 = mae(y_test, y_pred_1)\n",
    "mse_1 = mse(y_test, y_pred_1)\n",
    "mae_1, mse_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3f39ddaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 47.9949 - mse: 3405.3848\n",
      "Epoch 2/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 31.2165 - mse: 1304.2419\n",
      "Epoch 3/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 36.2073 - mse: 1973.0566\n",
      "Epoch 4/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 27.6350 - mse: 1116.7339\n",
      "Epoch 5/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.0644 - mse: 286.0727\n",
      "Epoch 6/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.9828 - mse: 173.0415\n",
      "Epoch 7/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 11.3128 - mse: 155.2308\n",
      "Epoch 8/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.1611 - mse: 166.4583\n",
      "Epoch 9/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 41.2179 - mse: 2694.8364\n",
      "Epoch 10/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 28.5170 - mse: 1147.7512\n",
      "Epoch 11/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.3947 - mse: 118.3282\n",
      "Epoch 12/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 26.3281 - mse: 958.8872\n",
      "Epoch 13/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 14.4437 - mse: 278.7823\n",
      "Epoch 14/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 29.8446 - mse: 1422.4298\n",
      "Epoch 15/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 19.7050 - mse: 543.2274\n",
      "Epoch 16/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.8368 - mse: 126.4146\n",
      "Epoch 17/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 17.6417 - mse: 410.6685\n",
      "Epoch 18/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 14.1174 - mse: 314.0124\n",
      "Epoch 19/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.9568 - mse: 303.4612\n",
      "Epoch 20/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 11.2085 - mse: 146.4401\n",
      "Epoch 21/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 17.2778 - mse: 422.7348\n",
      "Epoch 22/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.5155 - mse: 331.6179\n",
      "Epoch 23/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.2402 - mse: 118.6520\n",
      "Epoch 24/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.2647 - mse: 408.4778\n",
      "Epoch 25/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 15.9388 - mse: 333.7933\n",
      "Epoch 26/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 20.9296 - mse: 649.2859\n",
      "Epoch 27/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 25.9656 - mse: 1051.9567\n",
      "Epoch 28/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 18.4635 - mse: 539.5651\n",
      "Epoch 29/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.2432 - mse: 97.6636\n",
      "Epoch 30/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 29.0793 - mse: 1510.6503\n",
      "Epoch 31/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 52.6675 - mse: 4959.9316\n",
      "Epoch 32/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.9245 - mse: 206.8577\n",
      "Epoch 33/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.5160 - mse: 331.7204\n",
      "Epoch 34/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.6104 - mse: 209.8341\n",
      "Epoch 35/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.2035 - mse: 91.7623\n",
      "Epoch 36/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 16.5026 - mse: 395.8776\n",
      "Epoch 37/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.0787 - mse: 192.2559\n",
      "Epoch 38/100\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 18.2073 - mse: 436.6569\n",
      "Epoch 39/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 19.1400 - mse: 533.2651\n",
      "Epoch 40/100\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 20.5020 - mse: 614.0172\n",
      "Epoch 41/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 14.8236 - mse: 275.1148\n",
      "Epoch 42/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.2161 - mse: 182.8412\n",
      "Epoch 43/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.7008 - mse: 163.7513\n",
      "Epoch 44/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 32.3178 - mse: 1718.2545\n",
      "Epoch 45/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.4827 - mse: 202.5451\n",
      "Epoch 46/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.5563 - mse: 461.9031\n",
      "Epoch 47/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.7413 - mse: 338.7588\n",
      "Epoch 48/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.3669 - mse: 112.6942\n",
      "Epoch 49/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.9731 - mse: 268.6779\n",
      "Epoch 50/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.8157 - mse: 215.0451\n",
      "Epoch 51/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 14.8291 - mse: 308.9947\n",
      "Epoch 52/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 18.7164 - mse: 528.8525\n",
      "Epoch 53/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 24.1554 - mse: 827.6555\n",
      "Epoch 54/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 23.2097 - mse: 807.1805\n",
      "Epoch 55/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 24.0670 - mse: 871.7466\n",
      "Epoch 56/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 11.1727 - mse: 171.1037\n",
      "Epoch 57/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.1316 - mse: 214.6985\n",
      "Epoch 58/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 9.8199 - mse: 106.4300\n",
      "Epoch 59/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.3202 - mse: 250.1523\n",
      "Epoch 60/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.8798 - mse: 139.6133\n",
      "Epoch 61/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.4847 - mse: 242.2895\n",
      "Epoch 62/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 17.5225 - mse: 468.5732\n",
      "Epoch 63/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.1656 - mse: 87.7215\n",
      "Epoch 64/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 18.3940 - mse: 491.7972\n",
      "Epoch 65/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.1212 - mse: 115.0374\n",
      "Epoch 66/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 24.2610 - mse: 888.3806\n",
      "Epoch 67/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.8921 - mse: 142.8712\n",
      "Epoch 68/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.7735 - mse: 159.2936\n",
      "Epoch 69/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 23.2485 - mse: 781.2389\n",
      "Epoch 70/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.8161 - mse: 143.4943\n",
      "Epoch 71/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 15.9820 - mse: 347.8144\n",
      "Epoch 72/100\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.1439 - mse: 109.7109\n",
      "Epoch 73/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.4657 - mse: 153.1948\n",
      "Epoch 74/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 28.1573 - mse: 1110.6033\n",
      "Epoch 75/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.1977 - mse: 145.5360\n",
      "Epoch 76/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 13.1476 - mse: 218.7382\n",
      "Epoch 77/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 18.3854 - mse: 530.1138\n",
      "Epoch 78/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 9.0165 - mse: 91.4436\n",
      "Epoch 79/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 23.4342 - mse: 821.3904\n",
      "Epoch 80/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 26.1042 - mse: 1046.9373\n",
      "Epoch 81/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 11.3872 - mse: 158.2227\n",
      "Epoch 82/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 12.4971 - mse: 227.6685\n",
      "Epoch 83/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.1857 - mse: 384.2226\n",
      "Epoch 84/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 6.5962 - mse: 64.5777\n",
      "Epoch 85/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 20.2889 - mse: 575.5942\n",
      "Epoch 86/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.1678 - mse: 115.7113\n",
      "Epoch 87/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 24.3315 - mse: 828.3157\n",
      "Epoch 88/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 18.9969 - mse: 512.5504\n",
      "Epoch 89/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 7.1650 - mse: 70.2278\n",
      "Epoch 90/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 18.2607 - mse: 494.9502\n",
      "Epoch 91/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 13.3104 - mse: 255.9959\n",
      "Epoch 92/100\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 14.8614 - mse: 311.1474\n",
      "Epoch 93/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 11.6534 - mse: 250.0413\n",
      "Epoch 94/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 16.2099 - mse: 371.4369\n",
      "Epoch 95/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 15.5523 - mse: 322.3720\n",
      "Epoch 96/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 15.0874 - mse: 324.7144\n",
      "Epoch 97/100\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 10.9161 - mse: 188.1679\n",
      "Epoch 98/100\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 14.3770 - mse: 286.8979\n",
      "Epoch 99/100\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.3912 - mse: 251.9421\n",
      "Epoch 100/100\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 20.0188 - mse: 585.5561\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x14efd4db640>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "model_2 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(10, input_shape=[1]),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model_2.compile(loss=tf.keras.losses.mae,\n",
    "             optimizer=tf.keras.optimizers.SGD(),\n",
    "             metrics=[\"mse\"])\n",
    "\n",
    "model_2.fit(X_train, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dae7345d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 21ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make and plot predictions for model_2\n",
    "\n",
    "y_preds_2 = model_2.predict(X_test)\n",
    "plot_prediction(predictions=y_preds_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "675a05dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(), dtype=float32, numpy=29.525415>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=902.5707>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate model_2 evaluation metrics\n",
    "\n",
    "mae_2 = mae(y_test, y_preds_2)\n",
    "mse_2 = mse(y_test, y_preds_2)\n",
    "mae_2, mse_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2ad44f7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 31.6827 - mse: 1405.5219\n",
      "Epoch 2/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.4394 - mse: 270.8316\n",
      "Epoch 3/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 18.7728 - mse: 576.2275\n",
      "Epoch 4/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 31.7112 - mse: 1510.3390\n",
      "Epoch 5/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.7716 - mse: 275.4280\n",
      "Epoch 6/250\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 12.5438 - mse: 242.2212\n",
      "Epoch 7/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.9337 - mse: 273.4825\n",
      "Epoch 8/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 14.5371 - mse: 355.0155\n",
      "Epoch 9/250\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 4.8482 - mse: 107.2412\n",
      "Epoch 10/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 14.3372 - mse: 321.6202\n",
      "Epoch 11/250\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 13.1148 - mse: 266.5577\n",
      "Epoch 12/250\n",
      "2/2 [==============================] - 0s 10ms/step - loss: 24.1457 - mse: 857.3558\n",
      "Epoch 13/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 20.5880 - mse: 586.6232\n",
      "Epoch 14/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 19.1269 - mse: 522.9544\n",
      "Epoch 15/250\n",
      "2/2 [==============================] - 0s 9ms/step - loss: 9.5114 - mse: 213.4911\n",
      "Epoch 16/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.5913 - mse: 258.6447\n",
      "Epoch 17/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 19.9739 - mse: 636.2867\n",
      "Epoch 18/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 31.9901 - mse: 1512.6189\n",
      "Epoch 19/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 14.8108 - mse: 357.4597\n",
      "Epoch 20/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.8446 - mse: 259.3551\n",
      "Epoch 21/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 19.0759 - mse: 585.3984\n",
      "Epoch 22/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 35.8013 - mse: 1901.3037\n",
      "Epoch 23/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 4.9874 - mse: 120.6357\n",
      "Epoch 24/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.3790 - mse: 264.2577\n",
      "Epoch 25/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 20.6390 - mse: 651.4382\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "2/2 [==============================] - 0s 6ms/step - loss: 19.6697 - mse: 638.3617\n",
      "Epoch 197/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 26.6122 - mse: 1040.7273\n",
      "Epoch 198/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 16.1906 - mse: 394.4161\n",
      "Epoch 199/250\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 9.8095 - mse: 250.4018\n",
      "Epoch 200/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 8.8104 - mse: 331.6845\n",
      "Epoch 201/250\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 12.9866 - mse: 271.6253\n",
      "Epoch 202/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 22.9529 - mse: 761.6857\n",
      "Epoch 203/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 21.5320 - mse: 642.5554\n",
      "Epoch 204/250\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 16.0814 - mse: 394.9982\n",
      "Epoch 205/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 10.2287 - mse: 319.5731\n",
      "Epoch 206/250\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 15.1050 - mse: 369.7192\n",
      "Epoch 207/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 28.0679 - mse: 1109.5785\n",
      "Epoch 208/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 12.1037 - mse: 292.2481\n",
      "Epoch 209/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 8.0449 - mse: 276.9446\n",
      "Epoch 210/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.4291 - mse: 202.5367\n",
      "Epoch 211/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 12.3663 - mse: 275.1107\n",
      "Epoch 212/250\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 25.9820 - mse: 981.2567\n",
      "Epoch 213/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 9.7843 - mse: 221.0625\n",
      "Epoch 214/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.1542 - mse: 254.7840\n",
      "Epoch 215/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 25.1296 - mse: 947.7082\n",
      "Epoch 216/250\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 13.0250 - mse: 294.1922\n",
      "Epoch 217/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 7.0966 - mse: 141.5227\n",
      "Epoch 218/250\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 14.6935 - mse: 299.2465\n",
      "Epoch 219/250\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 17.0398 - mse: 442.8216\n",
      "Epoch 220/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 14.0537 - mse: 314.1232\n",
      "Epoch 221/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 33.9975 - mse: 1606.7639\n",
      "Epoch 222/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 13.5989 - mse: 247.7998\n",
      "Epoch 223/250\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 12.2364 - mse: 232.5902\n",
      "Epoch 224/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.8270 - mse: 305.4277\n",
      "Epoch 225/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 33.5505 - mse: 1581.2159\n",
      "Epoch 226/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 14.5119 - mse: 301.9746\n",
      "Epoch 227/250\n",
      "2/2 [==============================] - 0s 12ms/step - loss: 24.4611 - mse: 871.2155\n",
      "Epoch 228/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.0438 - mse: 414.4984\n",
      "Epoch 229/250\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 9.6744 - mse: 358.6902\n",
      "Epoch 230/250\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 13.4823 - mse: 267.5342\n",
      "Epoch 231/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 18.6271 - mse: 566.9669\n",
      "Epoch 232/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 31.4538 - mse: 1437.4484\n",
      "Epoch 233/250\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 23.0620 - mse: 746.1027\n",
      "Epoch 234/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.4944 - mse: 231.6373\n",
      "Epoch 235/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 13.0106 - mse: 241.4244\n",
      "Epoch 236/250\n",
      "2/2 [==============================] - 0s 8ms/step - loss: 18.6212 - mse: 573.6160\n",
      "Epoch 237/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 15.2116 - mse: 335.2927\n",
      "Epoch 238/250\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 16.2917 - mse: 438.7240\n",
      "Epoch 239/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 24.3399 - mse: 868.1652\n",
      "Epoch 240/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 19.6045 - mse: 533.6582\n",
      "Epoch 241/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 9.6623 - mse: 485.9145\n",
      "Epoch 242/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 16.0257 - mse: 448.7606\n",
      "Epoch 243/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 16.5579 - mse: 400.0844\n",
      "Epoch 244/250\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 14.6175 - mse: 298.8914\n",
      "Epoch 245/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 17.1368 - mse: 500.1782\n",
      "Epoch 246/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 22.7230 - mse: 780.2597\n",
      "Epoch 247/250\n",
      "2/2 [==============================] - 0s 7ms/step - loss: 27.7928 - mse: 1108.2290\n",
      "Epoch 248/250\n",
      "2/2 [==============================] - 0s 6ms/step - loss: 11.1740 - mse: 245.1801\n",
      "Epoch 249/250\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 13.3119 - mse: 251.6176\n",
      "Epoch 250/250\n",
      "2/2 [==============================] - 0s 5ms/step - loss: 25.8675 - mse: 988.2850\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x14efdb2e220>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Build Model 3\n",
    "\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model_3 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(1024, input_shape=[1]),\n",
    "    tf.keras.layers.Dense(512),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model_3.compile(loss=tf.keras.losses.mae,\n",
    "             optimizer=tf.keras.optimizers.Adam(),\n",
    "             metrics=[\"mae\"])\n",
    "\n",
    "model_2.fit(X_train, y_train, epochs=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "797ab778",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x0000014EFDC290D0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 62ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_preds_3 = model_3.predict(X_test)\n",
    "plot_prediction(predictions=y_preds_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0f91af60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(), dtype=float32, numpy=122.38518>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=15252.166>)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae_3 = mae(y_test, y_preds_3)\n",
    "mse_3 = mse(y_test, y_preds_3)\n",
    "mae_3, mse_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c17a0377",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>model_1</td>\n",
       "      <td>8.666227</td>\n",
       "      <td>902.570679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>model_2</td>\n",
       "      <td>29.525415</td>\n",
       "      <td>902.570679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>model_3</td>\n",
       "      <td>122.385178</td>\n",
       "      <td>15252.166016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     model         mae           mse\n",
       "0  model_1    8.666227    902.570679\n",
       "1  model_2   29.525415    902.570679\n",
       "2  model_3  122.385178  15252.166016"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compare Results\n",
    "\n",
    "# Let's create a pandas df to compare\n",
    "import pandas as pd\n",
    "\n",
    "model_results = [[\"model_1\", mae_1.numpy(), mse_2.numpy()],\n",
    "                [\"model_2\", mae_2.numpy(), mse_2.numpy()],\n",
    "                [\"model_3\", mae_3.numpy(), mse_3.numpy()]]\n",
    "\n",
    "all_results = pd.DataFrame(model_results, columns=[\"model\",\"mae\",\"mse\"])\n",
    "all_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "19675f30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (Dense)         (None, 1)                 2         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5388d01d",
   "metadata": {},
   "source": [
    "**Note:** One of your main goals should be to minimize the time between your experiments. The more experiments you do,\n",
    "the more things you'll figure out which don't work and in turn, get closer to figuring out what does work. Remember the machine learning practicioner's motto: \"Experiment, Experiment, Experiment\". Also remember to visualize all the steps."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "590fd465",
   "metadata": {},
   "source": [
    "#### Tracking your experiments\n",
    "\n",
    "One really good habit in machine learning modelling is to traxck the results of your experiments.\n",
    "and when doing so it can be tedious if you are running several experiments.\n",
    "Luckily there are tools\n",
    "* Tensorboard - a component of the Tensorflow library to help track modelling experiments.\n",
    "* Weights and Biases - another tool."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a031e732",
   "metadata": {},
   "source": [
    "## Saving our models\n",
    "\n",
    "Saving our model allow us to use them outsite the jupyter notebook. To be able to use in websites and mobile apps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "04d97069",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model_2_SavedModelFormat\\assets\n"
     ]
    }
   ],
   "source": [
    "#Save a model using the saved model format\n",
    "\n",
    "model_1.save(\"Model_2_SavedModelFormat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5a16a4de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save model using the HDF5 format\n",
    "\n",
    "model_1.save(\"Model_2_HDF5_Format.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d3f1fb",
   "metadata": {},
   "source": [
    "## Load a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e9d9374c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (Dense)         (None, 1)                 2         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "loaded_SaveModel_format = tf.keras.models.load_model(\"Model_2_HDF5_Format.h5\")\n",
    "loaded_SaveModel_format.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ce8848a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (Dense)         (None, 1)                 2         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8070abd",
   "metadata": {},
   "source": [
    "# Larger Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "aad8151c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e157a30a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
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       "      <td>southwest</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
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       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the insurance dataset\n",
    "\n",
    "insurance = pd.read_csv(\"insurance.csv\")\n",
    "insurance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5a630a3d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "    <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",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <th>1337</th>\n",
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       "      <td>29.070</td>\n",
       "      <td>0</td>\n",
       "      <td>29141.36030</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>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>"
      ],
      "text/plain": [
       "      age     bmi  children      charges  sex_female  sex_male  smoker_no  \\\n",
       "0      19  27.900         0  16884.92400           1         0          0   \n",
       "1      18  33.770         1   1725.55230           0         1          1   \n",
       "2      28  33.000         3   4449.46200           0         1          1   \n",
       "3      33  22.705         0  21984.47061           0         1          1   \n",
       "4      32  28.880         0   3866.85520           0         1          1   \n",
       "...   ...     ...       ...          ...         ...       ...        ...   \n",
       "1333   50  30.970         3  10600.54830           0         1          1   \n",
       "1334   18  31.920         0   2205.98080           1         0          1   \n",
       "1335   18  36.850         0   1629.83350           1         0          1   \n",
       "1336   21  25.800         0   2007.94500           1         0          1   \n",
       "1337   61  29.070         0  29141.36030           1         0          0   \n",
       "\n",
       "      smoker_yes  region_northeast  region_northwest  region_southeast  \\\n",
       "0              1                 0                 0                 0   \n",
       "1              0                 0                 0                 1   \n",
       "2              0                 0                 0                 1   \n",
       "3              0                 0                 1                 0   \n",
       "4              0                 0                 1                 0   \n",
       "...          ...               ...               ...               ...   \n",
       "1333           0                 0                 1                 0   \n",
       "1334           0                 1                 0                 0   \n",
       "1335           0                 0                 0                 1   \n",
       "1336           0                 0                 0                 0   \n",
       "1337           1                 0                 1                 0   \n",
       "\n",
       "      region_southwest  \n",
       "0                    1  \n",
       "1                    0  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    0  \n",
       "...                ...  \n",
       "1333                 0  \n",
       "1334                 0  \n",
       "1335                 0  \n",
       "1336                 1  \n",
       "1337                 0  \n",
       "\n",
       "[1338 rows x 12 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#OneHotEncode\n",
    "insurance_one_hot = pd.get_dummies(insurance)\n",
    "insurance_one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7db12b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create X and y (featues and labels)\n",
    "\n",
    "X = insurance_one_hot.drop(\"charges\", axis=1)\n",
    "y = insurance_one_hot[\"charges\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "90b830d5",
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "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  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5b0f2a57",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "72a8dfbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1338, 1070, 268)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "len(X), len(X_train), len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a31c1c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>560</th>\n",
       "      <td>46</td>\n",
       "      <td>19.950</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>47</td>\n",
       "      <td>24.320</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>1142</th>\n",
       "      <td>52</td>\n",
       "      <td>24.860</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>969</th>\n",
       "      <td>39</td>\n",
       "      <td>34.320</td>\n",
       "      <td>5</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>486</th>\n",
       "      <td>54</td>\n",
       "      <td>21.470</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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",
       "    </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>1095</th>\n",
       "      <td>18</td>\n",
       "      <td>31.350</td>\n",
       "      <td>4</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>1130</th>\n",
       "      <td>39</td>\n",
       "      <td>23.870</td>\n",
       "      <td>5</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>1294</th>\n",
       "      <td>58</td>\n",
       "      <td>25.175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>860</th>\n",
       "      <td>37</td>\n",
       "      <td>47.600</td>\n",
       "      <td>2</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>1126</th>\n",
       "      <td>55</td>\n",
       "      <td>29.900</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1070 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     bmi  children  sex_female  sex_male  smoker_no  smoker_yes  \\\n",
       "560    46  19.950         2           1         0          1           0   \n",
       "1285   47  24.320         0           1         0          1           0   \n",
       "1142   52  24.860         0           1         0          1           0   \n",
       "969    39  34.320         5           1         0          1           0   \n",
       "486    54  21.470         3           1         0          1           0   \n",
       "...   ...     ...       ...         ...       ...        ...         ...   \n",
       "1095   18  31.350         4           1         0          1           0   \n",
       "1130   39  23.870         5           1         0          1           0   \n",
       "1294   58  25.175         0           0         1          1           0   \n",
       "860    37  47.600         2           1         0          0           1   \n",
       "1126   55  29.900         0           0         1          1           0   \n",
       "\n",
       "      region_northeast  region_northwest  region_southeast  region_southwest  \n",
       "560                  0                 1                 0                 0  \n",
       "1285                 1                 0                 0                 0  \n",
       "1142                 0                 0                 1                 0  \n",
       "969                  0                 0                 1                 0  \n",
       "486                  0                 1                 0                 0  \n",
       "...                ...               ...               ...               ...  \n",
       "1095                 1                 0                 0                 0  \n",
       "1130                 0                 0                 1                 0  \n",
       "1294                 1                 0                 0                 0  \n",
       "860                  0                 0                 0                 1  \n",
       "1126                 0                 0                 0                 1  \n",
       "\n",
       "[1070 rows x 11 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6a81b0ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 8704.5986 - mae: 8704.5986\n",
      "Epoch 2/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7877.9980 - mae: 7877.9980\n",
      "Epoch 3/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7567.6865 - mae: 7567.6865\n",
      "Epoch 4/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7605.0386 - mae: 7605.0386\n",
      "Epoch 5/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7718.4116 - mae: 7718.4116\n",
      "Epoch 6/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7634.9849 - mae: 7634.9849\n",
      "Epoch 7/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7531.3369 - mae: 7531.3369\n",
      "Epoch 8/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 7722.1553 - mae: 7722.1553\n",
      "Epoch 9/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7575.5918 - mae: 7575.5918\n",
      "Epoch 10/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7675.9102 - mae: 7675.9102\n",
      "Epoch 11/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7516.9150 - mae: 7516.9150\n",
      "Epoch 12/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7468.0200 - mae: 7468.0200\n",
      "Epoch 13/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7681.2422 - mae: 7681.2422\n",
      "Epoch 14/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7694.2798 - mae: 7694.2798\n",
      "Epoch 15/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 7387.9932 - mae: 7387.9932\n",
      "Epoch 16/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7768.9116 - mae: 7768.9116\n",
      "Epoch 17/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7515.2139 - mae: 7515.2139\n",
      "Epoch 18/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7731.0981 - mae: 7731.0981\n",
      "Epoch 19/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7739.8403 - mae: 7739.8403\n",
      "Epoch 20/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7938.1055 - mae: 7938.1055\n",
      "Epoch 21/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7520.7827 - mae: 7520.7827\n",
      "Epoch 22/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 7852.8384 - mae: 7852.8384\n",
      "Epoch 23/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7631.5659 - mae: 7631.5659\n",
      "Epoch 24/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7518.8223 - mae: 7518.8223\n",
      "Epoch 25/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7640.1904 - mae: 7640.1904\n",
      "Epoch 26/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 7676.9214 - mae: 7676.9214\n",
      "Epoch 27/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 7535.2510 - mae: 7535.2510\n",
      "Epoch 28/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7463.2705 - mae: 7463.2705\n",
      "Epoch 29/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7659.4185 - mae: 7659.4185\n",
      "Epoch 30/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7438.6787 - mae: 7438.6787\n",
      "Epoch 31/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7570.1187 - mae: 7570.1187\n",
      "Epoch 32/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7424.6890 - mae: 7424.6890\n",
      "Epoch 33/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7397.0884 - mae: 7397.0884\n",
      "Epoch 34/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7481.7744 - mae: 7481.7744\n",
      "Epoch 35/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7543.0435 - mae: 7543.0435\n",
      "Epoch 36/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7567.7710 - mae: 7567.7710\n",
      "Epoch 37/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7699.5513 - mae: 7699.5513\n",
      "Epoch 38/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7652.5923 - mae: 7652.5923\n",
      "Epoch 39/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7549.5308 - mae: 7549.5308\n",
      "Epoch 40/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7381.1807 - mae: 7381.1807\n",
      "Epoch 41/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7724.0479 - mae: 7724.0479\n",
      "Epoch 42/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7672.2725 - mae: 7672.2725\n",
      "Epoch 43/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7617.1265 - mae: 7617.1265\n",
      "Epoch 44/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7446.4575 - mae: 7446.4575\n",
      "Epoch 45/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7337.7690 - mae: 7337.7690\n",
      "Epoch 46/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7390.3701 - mae: 7390.3701\n",
      "Epoch 47/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7471.4385 - mae: 7471.4385\n",
      "Epoch 48/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7501.5312 - mae: 7501.5312\n",
      "Epoch 49/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7551.7124 - mae: 7551.7124\n",
      "Epoch 50/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7488.1206 - mae: 7488.1206\n",
      "Epoch 51/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7427.3901 - mae: 7427.3901\n",
      "Epoch 52/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7371.8667 - mae: 7371.8667\n",
      "Epoch 53/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7642.4658 - mae: 7642.4658\n",
      "Epoch 54/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 7217.1802 - mae: 7217.1802\n",
      "Epoch 55/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7039.4814 - mae: 7039.4814\n",
      "Epoch 56/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7276.9805 - mae: 7276.9805\n",
      "Epoch 57/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7370.0249 - mae: 7370.0249\n",
      "Epoch 58/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7578.7632 - mae: 7578.7632\n",
      "Epoch 59/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7551.1069 - mae: 7551.1069\n",
      "Epoch 60/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7356.3003 - mae: 7356.3003\n",
      "Epoch 61/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7201.9531 - mae: 7201.9531\n",
      "Epoch 62/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7382.8550 - mae: 7382.8550\n",
      "Epoch 63/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7360.2319 - mae: 7360.2319\n",
      "Epoch 64/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7292.2598 - mae: 7292.2598\n",
      "Epoch 65/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7511.6147 - mae: 7511.6147\n",
      "Epoch 66/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7420.1206 - mae: 7420.1206\n",
      "Epoch 67/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7322.3501 - mae: 7322.3501\n",
      "Epoch 68/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7564.2578 - mae: 7564.2578\n",
      "Epoch 69/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7557.3975 - mae: 7557.3975\n",
      "Epoch 70/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7675.0151 - mae: 7675.0151\n",
      "Epoch 71/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7314.6997 - mae: 7314.6997\n",
      "Epoch 72/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7261.0972 - mae: 7261.0972\n",
      "Epoch 73/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7124.6567 - mae: 7124.6567\n",
      "Epoch 74/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7388.4976 - mae: 7388.4976\n",
      "Epoch 75/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7304.4263 - mae: 7304.4263\n",
      "Epoch 76/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7152.0942 - mae: 7152.0942\n",
      "Epoch 77/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7311.5049 - mae: 7311.5049\n",
      "Epoch 78/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7140.3428 - mae: 7140.3428\n",
      "Epoch 79/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7386.9570 - mae: 7386.9570\n",
      "Epoch 80/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7195.8550 - mae: 7195.8550\n",
      "Epoch 81/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7287.6997 - mae: 7287.6997\n",
      "Epoch 82/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7133.2637 - mae: 7133.2637\n",
      "Epoch 83/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7116.1265 - mae: 7116.1265\n",
      "Epoch 84/100\n",
      "34/34 [==============================] - 0s 5ms/step - loss: 7406.6992 - mae: 7406.6992\n",
      "Epoch 85/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7348.2974 - mae: 7348.2974\n",
      "Epoch 86/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7523.9067 - mae: 7523.9067\n",
      "Epoch 87/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7223.1699 - mae: 7223.1699\n",
      "Epoch 88/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7384.2969 - mae: 7384.2969\n",
      "Epoch 89/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7422.1328 - mae: 7422.1328\n",
      "Epoch 90/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 6963.4639 - mae: 6963.4639\n",
      "Epoch 91/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7235.7856 - mae: 7235.7856\n",
      "Epoch 92/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7430.3423 - mae: 7430.3423\n",
      "Epoch 93/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7357.2471 - mae: 7357.2471\n",
      "Epoch 94/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7327.2905 - mae: 7327.2905\n",
      "Epoch 95/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7416.1201 - mae: 7416.1201\n",
      "Epoch 96/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7276.2300 - mae: 7276.2300\n",
      "Epoch 97/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7140.7446 - mae: 7140.7446\n",
      "Epoch 98/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7457.2612 - mae: 7457.2612\n",
      "Epoch 99/100\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 7399.7627 - mae: 7399.7627\n",
      "Epoch 100/100\n",
      "34/34 [==============================] - 0s 4ms/step - loss: 7383.7192 - mae: 7383.7192\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1501dd041c0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Build Neural network\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "insurance_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(10),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "insurance_model.compile(loss=tf.keras.losses.mae,\n",
    "                       optimizer=tf.keras.optimizers.SGD(),\n",
    "                       metrics=[\"mae\"])\n",
    "\n",
    "insurance_model.fit(X_train, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "648b9262",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 5ms/step - loss: 7089.2505 - mae: 7089.2505\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[7089.25048828125, 7089.25048828125]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the results on the insurance model on the test data\n",
    "\n",
    "insurance_model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8b1762fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "34/34 [==============================] - 1s 11ms/step - loss: 13310.1689 - mae: 13310.1689\n",
      "Epoch 2/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 13144.8672 - mae: 13144.8672\n",
      "Epoch 3/100\n",
      "34/34 [==============================] - 0s 15ms/step - loss: 12794.5488 - mae: 12794.5488\n",
      "Epoch 4/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 12096.0938 - mae: 12096.0938\n",
      "Epoch 5/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 10927.0195 - mae: 10927.0195\n",
      "Epoch 6/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 9458.2256 - mae: 9458.2256\n",
      "Epoch 7/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 8146.3140 - mae: 8146.3140\n",
      "Epoch 8/100\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 7541.8521 - mae: 7541.8521\n",
      "Epoch 9/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 7447.7471 - mae: 7447.7471\n",
      "Epoch 10/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 7427.1890 - mae: 7427.1890\n",
      "Epoch 11/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 7408.1929 - mae: 7408.1929\n",
      "Epoch 12/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 7385.6250 - mae: 7385.6250\n",
      "Epoch 13/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 7364.7251 - mae: 7364.7251\n",
      "Epoch 14/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 7341.8418 - mae: 7341.8418\n",
      "Epoch 15/100\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 7322.1841 - mae: 7322.1841\n",
      "Epoch 16/100\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 7298.9072 - mae: 7298.9072\n",
      "Epoch 17/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 7278.6714 - mae: 7278.6714\n",
      "Epoch 18/100\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 7254.0063 - mae: 7254.0063\n",
      "Epoch 19/100\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 7231.4917 - mae: 7231.4917\n",
      "Epoch 20/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 7207.7041 - mae: 7207.7041\n",
      "Epoch 21/100\n",
      "34/34 [==============================] - 0s 15ms/step - loss: 7187.7119 - mae: 7187.7119\n",
      "Epoch 22/100\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 7159.8325 - mae: 7159.8325\n",
      "Epoch 23/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 7134.7402 - mae: 7134.7402\n",
      "Epoch 24/100\n",
      "34/34 [==============================] - 0s 14ms/step - loss: 7109.1587 - mae: 7109.1587\n",
      "Epoch 25/100\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 7089.9575 - mae: 7089.9575\n",
      "Epoch 26/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 7058.3433 - mae: 7058.3433\n",
      "Epoch 27/100\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 7028.7539 - mae: 7028.7539\n",
      "Epoch 28/100\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 6999.9785 - mae: 6999.9785\n",
      "Epoch 29/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 6971.0054 - mae: 6971.0054\n",
      "Epoch 30/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 6943.3364 - mae: 6943.3364\n",
      "Epoch 31/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 6911.1914 - mae: 6911.1914\n",
      "Epoch 32/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 6882.9990 - mae: 6882.9990\n",
      "Epoch 33/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 6851.0303 - mae: 6851.0303\n",
      "Epoch 34/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 6817.8501 - mae: 6817.8501\n",
      "Epoch 35/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 6784.0723 - mae: 6784.0723\n",
      "Epoch 36/100\n",
      "34/34 [==============================] - 0s 13ms/step - loss: 6748.8804 - mae: 6748.8804\n",
      "Epoch 37/100\n",
      "34/34 [==============================] - 0s 14ms/step - loss: 6711.8652 - mae: 6711.8652\n",
      "Epoch 38/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 6680.0850 - mae: 6680.0850\n",
      "Epoch 39/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 6640.9614 - mae: 6640.9614\n",
      "Epoch 40/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 6605.3423 - mae: 6605.3423\n",
      "Epoch 41/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 6571.6812 - mae: 6571.6812\n",
      "Epoch 42/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 6545.8052 - mae: 6545.8052\n",
      "Epoch 43/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 6514.5596 - mae: 6514.5596\n",
      "Epoch 44/100\n",
      "34/34 [==============================] - 0s 12ms/step - loss: 6491.3828 - mae: 6491.3828\n",
      "Epoch 45/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 6478.3926 - mae: 6478.3926\n",
      "Epoch 46/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 6460.3213 - mae: 6460.3213\n",
      "Epoch 47/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 6442.9189 - mae: 6442.9189\n",
      "Epoch 48/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 6428.7354 - mae: 6428.7354\n",
      "Epoch 49/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 6415.1006 - mae: 6415.1006\n",
      "Epoch 50/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 6400.5327 - mae: 6400.5327\n",
      "Epoch 51/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6386.3701 - mae: 6386.3701\n",
      "Epoch 52/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6374.5366 - mae: 6374.5366\n",
      "Epoch 53/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6359.2783 - mae: 6359.2783\n",
      "Epoch 54/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 6345.5928 - mae: 6345.5928\n",
      "Epoch 55/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 6331.0293 - mae: 6331.0293\n",
      "Epoch 56/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 6316.8003 - mae: 6316.8003\n",
      "Epoch 57/100\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 6303.0864 - mae: 6303.0864\n",
      "Epoch 58/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 6289.0806 - mae: 6289.0806\n",
      "Epoch 59/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6272.5571 - mae: 6272.5571\n",
      "Epoch 60/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6261.1108 - mae: 6261.1108\n",
      "Epoch 61/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 6240.3442 - mae: 6240.3442\n",
      "Epoch 62/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 6227.6768 - mae: 6227.6768\n",
      "Epoch 63/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 6208.6885 - mae: 6208.6885\n",
      "Epoch 64/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 6191.2852 - mae: 6191.2852\n",
      "Epoch 65/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 6172.9741 - mae: 6172.9741\n",
      "Epoch 66/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 6154.6924 - mae: 6154.6924\n",
      "Epoch 67/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 6140.8760 - mae: 6140.8760\n",
      "Epoch 68/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6116.9346 - mae: 6116.9346\n",
      "Epoch 69/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6100.1694 - mae: 6100.1694\n",
      "Epoch 70/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6078.2446 - mae: 6078.2446\n",
      "Epoch 71/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6057.7847 - mae: 6057.7847\n",
      "Epoch 72/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6037.4995 - mae: 6037.4995\n",
      "Epoch 73/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6021.5708 - mae: 6021.5708\n",
      "Epoch 74/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5991.5806 - mae: 5991.5806\n",
      "Epoch 75/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 5968.8276 - mae: 5968.8276\n",
      "Epoch 76/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5951.9839 - mae: 5951.9839\n",
      "Epoch 77/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 5918.7695 - mae: 5918.7695\n",
      "Epoch 78/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 5893.0371 - mae: 5893.0371\n",
      "Epoch 79/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5866.6411 - mae: 5866.6411\n",
      "Epoch 80/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5836.4214 - mae: 5836.4214\n",
      "Epoch 81/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 5809.6001 - mae: 5809.6001\n",
      "Epoch 82/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 5777.6772 - mae: 5777.6772\n",
      "Epoch 83/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5747.5127 - mae: 5747.5127\n",
      "Epoch 84/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5711.4307 - mae: 5711.4307\n",
      "Epoch 85/100\n",
      "34/34 [==============================] - 0s 6ms/step - loss: 5683.9258 - mae: 5683.9258\n",
      "Epoch 86/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5645.4956 - mae: 5645.4956\n",
      "Epoch 87/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5606.4492 - mae: 5606.4492\n",
      "Epoch 88/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5568.0264 - mae: 5568.0264\n",
      "Epoch 89/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5524.3223 - mae: 5524.3223\n",
      "Epoch 90/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5478.9634 - mae: 5478.9634\n",
      "Epoch 91/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5438.3369 - mae: 5438.3369\n",
      "Epoch 92/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5386.4058 - mae: 5386.4058\n",
      "Epoch 93/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5339.3247 - mae: 5339.3247\n",
      "Epoch 94/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 5287.7905 - mae: 5287.7905\n",
      "Epoch 95/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5232.2676 - mae: 5232.2676\n",
      "Epoch 96/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 5181.4766 - mae: 5181.4766\n",
      "Epoch 97/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 5124.0083 - mae: 5124.0083\n",
      "Epoch 98/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 5055.9043 - mae: 5055.9043\n",
      "Epoch 99/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 4991.0815 - mae: 4991.0815\n",
      "Epoch 100/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 4930.1128 - mae: 4930.1128\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x15020683d30>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's improve model\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "insurance_model_2 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(100),\n",
    "    tf.keras.layers.Dense(10),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "insurance_model_2.compile(loss=tf.keras.losses.mae,\n",
    "                         optimizer=tf.keras.optimizers.Adam(),\n",
    "                         metrics=[\"mae\"])\n",
    "\n",
    "insurance_model_2.fit(X_train, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "cb59456e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 6ms/step - loss: 4801.7056 - mae: 4801.7056\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[4801.70556640625, 4801.70556640625]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance_model_2.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4435c657",
   "metadata": {},
   "source": [
    "insurance_model_2 added new layer of 100 neurons, proceed to change optimizer to `Adam` and ran test. Results improved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "827437be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "34/34 [==============================] - 1s 7ms/step - loss: 13294.0312 - mae: 13294.0312\n",
      "Epoch 2/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 13141.6514 - mae: 13141.6514\n",
      "Epoch 3/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 12832.0088 - mae: 12832.0088\n",
      "Epoch 4/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 12225.7402 - mae: 12225.7402\n",
      "Epoch 5/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 11178.9023 - mae: 11178.9023\n",
      "Epoch 6/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 9749.1514 - mae: 9749.1514\n",
      "Epoch 7/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 8340.8701 - mae: 8340.8701\n",
      "Epoch 8/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7573.5493 - mae: 7573.5493\n",
      "Epoch 9/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7421.9473 - mae: 7421.9473\n",
      "Epoch 10/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7399.5161 - mae: 7399.5161\n",
      "Epoch 11/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7380.4512 - mae: 7380.4512\n",
      "Epoch 12/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7359.1016 - mae: 7359.1016\n",
      "Epoch 13/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7338.8428 - mae: 7338.8428\n",
      "Epoch 14/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7316.7559 - mae: 7316.7559\n",
      "Epoch 15/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7298.3228 - mae: 7298.3228\n",
      "Epoch 16/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7276.4043 - mae: 7276.4043\n",
      "Epoch 17/500\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 7256.4907 - mae: 7256.4907\n",
      "Epoch 18/500\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 7233.3931 - mae: 7233.3931\n",
      "Epoch 19/500\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "34/34 [==============================] - 0s 7ms/step - loss: 3483.8560 - mae: 3483.8560\n",
      "Epoch 350/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3480.7620 - mae: 3480.7620\n",
      "Epoch 351/500\n",
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      "Epoch 352/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3482.0085 - mae: 3482.0085\n",
      "Epoch 353/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3497.7507 - mae: 3497.7507\n",
      "Epoch 354/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.1968 - mae: 3478.1968\n",
      "Epoch 355/500\n",
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      "Epoch 356/500\n",
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      "Epoch 357/500\n",
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      "Epoch 358/500\n",
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      "Epoch 359/500\n",
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      "Epoch 360/500\n",
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      "Epoch 361/500\n",
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      "Epoch 362/500\n",
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      "Epoch 363/500\n",
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      "Epoch 364/500\n",
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      "Epoch 366/500\n",
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      "Epoch 367/500\n",
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      "Epoch 368/500\n",
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      "Epoch 369/500\n",
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      "Epoch 370/500\n",
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      "Epoch 371/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.1941 - mae: 3478.1941\n",
      "Epoch 372/500\n",
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      "Epoch 373/500\n",
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      "Epoch 374/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3487.6648 - mae: 3487.6648\n",
      "Epoch 375/500\n",
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      "Epoch 376/500\n",
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      "Epoch 377/500\n",
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      "Epoch 378/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3478.3811 - mae: 3478.3811\n",
      "Epoch 379/500\n",
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      "Epoch 380/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3481.4631 - mae: 3481.4631\n",
      "Epoch 381/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3491.5916 - mae: 3491.5916\n",
      "Epoch 382/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3481.8184 - mae: 3481.8184\n",
      "Epoch 383/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.9802 - mae: 3478.9802\n",
      "Epoch 384/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3499.5842 - mae: 3499.5842\n",
      "Epoch 385/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3485.9336 - mae: 3485.9336\n",
      "Epoch 386/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3487.4558 - mae: 3487.4558\n",
      "Epoch 387/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3480.7329 - mae: 3480.7329\n",
      "Epoch 388/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3479.8474 - mae: 3479.8474\n",
      "Epoch 389/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3488.7090 - mae: 3488.7090\n",
      "Epoch 390/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3496.7007 - mae: 3496.7007\n",
      "Epoch 391/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3482.7185 - mae: 3482.7185\n",
      "Epoch 392/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3484.6592 - mae: 3484.6592\n",
      "Epoch 393/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3494.2993 - mae: 3494.2993\n",
      "Epoch 394/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3476.4973 - mae: 3476.4973\n",
      "Epoch 395/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3487.7798 - mae: 3487.7798\n",
      "Epoch 396/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3480.5632 - mae: 3480.5632\n",
      "Epoch 397/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3480.5015 - mae: 3480.5015\n",
      "Epoch 398/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3479.5771 - mae: 3479.5771\n",
      "Epoch 399/500\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 3485.2803 - mae: 3485.2803\n",
      "Epoch 400/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3476.4502 - mae: 3476.4502\n",
      "Epoch 401/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3480.7334 - mae: 3480.7334\n",
      "Epoch 402/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34/34 [==============================] - 0s 8ms/step - loss: 3482.3840 - mae: 3482.3840\n",
      "Epoch 403/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3482.8813 - mae: 3482.8813\n",
      "Epoch 404/500\n",
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      "Epoch 405/500\n",
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      "Epoch 406/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3482.7126 - mae: 3482.7126\n",
      "Epoch 407/500\n",
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      "Epoch 408/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3480.9338 - mae: 3480.9338\n",
      "Epoch 409/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.9763 - mae: 3478.9763\n",
      "Epoch 410/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3477.5354 - mae: 3477.5354\n",
      "Epoch 411/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3489.1570 - mae: 3489.1570\n",
      "Epoch 412/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.5991 - mae: 3478.5991\n",
      "Epoch 413/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3476.9426 - mae: 3476.9426\n",
      "Epoch 414/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3480.4954 - mae: 3480.4954\n",
      "Epoch 415/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3485.4524 - mae: 3485.4524\n",
      "Epoch 416/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3475.6313 - mae: 3475.6313\n",
      "Epoch 417/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.2874 - mae: 3478.2874\n",
      "Epoch 418/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3492.0190 - mae: 3492.0190\n",
      "Epoch 419/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3479.3557 - mae: 3479.3557\n",
      "Epoch 420/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3488.2371 - mae: 3488.2371\n",
      "Epoch 421/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3478.4382 - mae: 3478.4382\n",
      "Epoch 422/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3485.2983 - mae: 3485.2983\n",
      "Epoch 423/500\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 3489.0273 - mae: 3489.0273\n",
      "Epoch 424/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3503.6284 - mae: 3503.6284\n",
      "Epoch 425/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3476.3552 - mae: 3476.3552\n",
      "Epoch 426/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3493.8076 - mae: 3493.8076\n",
      "Epoch 427/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.8953 - mae: 3478.8953\n",
      "Epoch 428/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.3562 - mae: 3478.3562\n",
      "Epoch 429/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3473.9048 - mae: 3473.9048\n",
      "Epoch 430/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3484.6201 - mae: 3484.6201\n",
      "Epoch 431/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3486.8960 - mae: 3486.8960\n",
      "Epoch 432/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3476.0874 - mae: 3476.0874\n",
      "Epoch 433/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3474.9309 - mae: 3474.9309\n",
      "Epoch 434/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3486.0364 - mae: 3486.0364\n",
      "Epoch 435/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3487.6541 - mae: 3487.6541\n",
      "Epoch 436/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3480.3335 - mae: 3480.3335\n",
      "Epoch 437/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3484.1765 - mae: 3484.1765\n",
      "Epoch 438/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3491.3550 - mae: 3491.3550\n",
      "Epoch 439/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3482.1096 - mae: 3482.1096\n",
      "Epoch 440/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3490.5808 - mae: 3490.5808\n",
      "Epoch 441/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3479.4321 - mae: 3479.4321\n",
      "Epoch 442/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3484.8743 - mae: 3484.8743\n",
      "Epoch 443/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3480.3799 - mae: 3480.3799\n",
      "Epoch 444/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3479.1819 - mae: 3479.1819\n",
      "Epoch 445/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3488.0393 - mae: 3488.0393\n",
      "Epoch 446/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3506.9514 - mae: 3506.9514\n",
      "Epoch 447/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.1226 - mae: 3478.1226\n",
      "Epoch 448/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3481.1248 - mae: 3481.1248\n",
      "Epoch 449/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3482.2812 - mae: 3482.2812\n",
      "Epoch 450/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3483.3328 - mae: 3483.3328\n",
      "Epoch 451/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3487.2500 - mae: 3487.2500\n",
      "Epoch 452/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3495.0000 - mae: 3495.0000\n",
      "Epoch 453/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3477.1414 - mae: 3477.1414\n",
      "Epoch 454/500\n",
      "34/34 [==============================] - 0s 10ms/step - loss: 3485.8599 - mae: 3485.8599\n",
      "Epoch 455/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3480.7529 - mae: 3480.7529\n",
      "Epoch 456/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3482.3125 - mae: 3482.3125\n",
      "Epoch 457/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3486.8513 - mae: 3486.8513\n",
      "Epoch 458/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3486.0298 - mae: 3486.0298\n",
      "Epoch 459/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.0051 - mae: 3478.0051\n",
      "Epoch 460/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3477.8643 - mae: 3477.8643\n",
      "Epoch 461/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3474.7546 - mae: 3474.7546\n",
      "Epoch 462/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3482.0874 - mae: 3482.0874\n",
      "Epoch 463/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3476.6294 - mae: 3476.6294\n",
      "Epoch 464/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.5952 - mae: 3478.5952\n",
      "Epoch 465/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3480.0916 - mae: 3480.0916\n",
      "Epoch 466/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3482.1404 - mae: 3482.1404\n",
      "Epoch 467/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3482.2454 - mae: 3482.2454\n",
      "Epoch 468/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3486.5696 - mae: 3486.5696\n",
      "Epoch 469/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3477.7468 - mae: 3477.7468\n",
      "Epoch 470/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3476.3630 - mae: 3476.3630\n",
      "Epoch 471/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3488.2783 - mae: 3488.2783\n",
      "Epoch 472/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3474.5762 - mae: 3474.5762\n",
      "Epoch 473/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.2253 - mae: 3478.2253\n",
      "Epoch 474/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3478.8032 - mae: 3478.8032\n",
      "Epoch 475/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3491.9775 - mae: 3491.9775\n",
      "Epoch 476/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3487.6418 - mae: 3487.6418\n",
      "Epoch 477/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3496.1006 - mae: 3496.1006\n",
      "Epoch 478/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3477.7087 - mae: 3477.7087\n",
      "Epoch 479/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3480.8726 - mae: 3480.8726\n",
      "Epoch 480/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3487.5659 - mae: 3487.5659\n",
      "Epoch 481/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3478.0662 - mae: 3478.0662\n",
      "Epoch 482/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3482.0215 - mae: 3482.0215\n",
      "Epoch 483/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3482.2087 - mae: 3482.2087\n",
      "Epoch 484/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3483.2595 - mae: 3483.2595\n",
      "Epoch 485/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3481.2332 - mae: 3481.2332\n",
      "Epoch 486/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3505.7986 - mae: 3505.7986\n",
      "Epoch 487/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3480.5918 - mae: 3480.5918\n",
      "Epoch 488/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3475.1956 - mae: 3475.1956\n",
      "Epoch 489/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3472.9128 - mae: 3472.9128\n",
      "Epoch 490/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3475.1123 - mae: 3475.1123\n",
      "Epoch 491/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3509.8081 - mae: 3509.8081\n",
      "Epoch 492/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3472.7891 - mae: 3472.7891\n",
      "Epoch 493/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3486.8977 - mae: 3486.8977\n",
      "Epoch 494/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3477.0737 - mae: 3477.0737\n",
      "Epoch 495/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3481.2444 - mae: 3481.2444\n",
      "Epoch 496/500\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3475.6238 - mae: 3475.6238\n",
      "Epoch 497/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3487.2268 - mae: 3487.2268\n",
      "Epoch 498/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3487.1963 - mae: 3487.1963\n",
      "Epoch 499/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3476.2112 - mae: 3476.2112\n",
      "Epoch 500/500\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3476.8506 - mae: 3476.8506\n"
     ]
    }
   ],
   "source": [
    "#New Experiment, train for longer\n",
    "\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "insurance_model_3 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(100),\n",
    "    tf.keras.layers.Dense(10),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "insurance_model_3.compile(loss=tf.keras.losses.mae,\n",
    "                         optimizer=tf.keras.optimizers.Adam(),\n",
    "                         metrics=[\"mae\"])\n",
    "\n",
    "history = insurance_model_3.fit(X_train, y_train, epochs=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0a5c8aff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 9ms/step - loss: 3497.4463 - mae: 3497.4463\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3497.4462890625, 3497.4462890625]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance_model_3.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3474504d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epochs')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot History (also known as loss curve or training curve)\n",
    "\n",
    "pd.DataFrame(history.history).plot()\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epochs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f3aa44",
   "metadata": {},
   "source": [
    "# Preprocessing data (normalization and standardization)\n",
    "\n",
    "In terms of scaling values, neural networks tend to prefer normalization.\n",
    "If you are not sure on which to use you could try both and see which performs better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a8805f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>"
      ],
      "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]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8430a246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X[\"age\"].plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e88a3468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X[\"bmi\"].plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7606dcc6",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"children\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d654b762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "\n",
    "# Read in the insurance dataframe\n",
    "\n",
    "insurance = pd.read_csv(\"insurance.csv\")\n",
    "insurance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "0f68237e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To prepare our data we can use some classes from Scikit Learn\n",
    "\n",
    "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 & y\n",
    "\n",
    "X = insurance.drop(\"charges\", axis=1)\n",
    "y = insurance[\"charges\"]\n",
    "\n",
    "# Build our train and test set\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_normal = ct.transform(X_train)\n",
    "X_test_normal = ct.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "68ff3f1d",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What does our data look like now\n",
    "X_train.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "418e3b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.60869565, 0.10734463, 0.4       , 1.        , 0.        ,\n",
       "       1.        , 0.        , 0.        , 1.        , 0.        ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_normal[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bac75c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1070, 6), (1070, 11))"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_train_normal.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f7b8f3",
   "metadata": {},
   "source": [
    "Data has been normalized and HotEncoded and is ready for a Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bd8c6deb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "34/34 [==============================] - 1s 8ms/step - loss: 13343.6338 - mae: 13343.6338\n",
      "Epoch 2/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 13335.5596 - mae: 13335.5596\n",
      "Epoch 3/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 13316.1416 - mae: 13316.1416\n",
      "Epoch 4/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 13275.0957 - mae: 13275.0957\n",
      "Epoch 5/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 13200.9492 - mae: 13200.9492\n",
      "Epoch 6/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 13082.3066 - mae: 13082.3066\n",
      "Epoch 7/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 12908.8145 - mae: 12908.8145\n",
      "Epoch 8/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 12670.3232 - mae: 12670.3232\n",
      "Epoch 9/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 12356.5596 - mae: 12356.5596\n",
      "Epoch 10/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 11961.9023 - mae: 11961.9023\n",
      "Epoch 11/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 11493.8916 - mae: 11493.8916\n",
      "Epoch 12/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 10988.4014 - mae: 10988.4014\n",
      "Epoch 13/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 10487.3154 - mae: 10487.3154\n",
      "Epoch 14/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 9988.2871 - mae: 9988.2871\n",
      "Epoch 15/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 9515.7822 - mae: 9515.7822\n",
      "Epoch 16/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 9094.7500 - mae: 9094.7500\n",
      "Epoch 17/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 8745.0957 - mae: 8745.0957\n",
      "Epoch 18/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 8461.9590 - mae: 8461.9590\n",
      "Epoch 19/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 8242.6230 - mae: 8242.6230\n",
      "Epoch 20/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 8093.6543 - mae: 8093.6543\n",
      "Epoch 21/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7983.2627 - mae: 7983.2627\n",
      "Epoch 22/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7907.6880 - mae: 7907.6880\n",
      "Epoch 23/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7847.7217 - mae: 7847.7217\n",
      "Epoch 24/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7794.8672 - mae: 7794.8672\n",
      "Epoch 25/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7756.0664 - mae: 7756.0664\n",
      "Epoch 26/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7704.6875 - mae: 7704.6875\n",
      "Epoch 27/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7662.8857 - mae: 7662.8857\n",
      "Epoch 28/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7620.6821 - mae: 7620.6821\n",
      "Epoch 29/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7578.4839 - mae: 7578.4839\n",
      "Epoch 30/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 7535.2798 - mae: 7535.2798\n",
      "Epoch 31/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7491.6846 - mae: 7491.6846\n",
      "Epoch 32/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7447.9077 - mae: 7447.9077\n",
      "Epoch 33/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7403.9395 - mae: 7403.9395\n",
      "Epoch 34/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7356.0293 - mae: 7356.0293\n",
      "Epoch 35/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7309.6152 - mae: 7309.6152\n",
      "Epoch 36/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7259.8770 - mae: 7259.8770\n",
      "Epoch 37/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7209.9585 - mae: 7209.9585\n",
      "Epoch 38/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7159.3545 - mae: 7159.3545\n",
      "Epoch 39/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 7105.0874 - mae: 7105.0874\n",
      "Epoch 40/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 7050.1846 - mae: 7050.1846\n",
      "Epoch 41/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6994.3081 - mae: 6994.3081\n",
      "Epoch 42/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6936.0830 - mae: 6936.0830\n",
      "Epoch 43/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 6874.1660 - mae: 6874.1660\n",
      "Epoch 44/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 6808.6392 - mae: 6808.6392\n",
      "Epoch 45/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6742.3154 - mae: 6742.3154\n",
      "Epoch 46/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6673.7134 - mae: 6673.7134\n",
      "Epoch 47/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6603.4492 - mae: 6603.4492\n",
      "Epoch 48/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6525.8438 - mae: 6525.8438\n",
      "Epoch 49/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6447.8872 - mae: 6447.8872\n",
      "Epoch 50/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6363.4126 - mae: 6363.4126\n",
      "Epoch 51/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6280.1206 - mae: 6280.1206\n",
      "Epoch 52/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6187.9160 - mae: 6187.9160\n",
      "Epoch 53/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 6093.1128 - mae: 6093.1128\n",
      "Epoch 54/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5995.8198 - mae: 5995.8198\n",
      "Epoch 55/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5889.8618 - mae: 5889.8618\n",
      "Epoch 56/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 5782.9561 - mae: 5782.9561\n",
      "Epoch 57/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 5668.7666 - mae: 5668.7666\n",
      "Epoch 58/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 5552.5537 - mae: 5552.5537\n",
      "Epoch 59/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 5436.1729 - mae: 5436.1729\n",
      "Epoch 60/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 5314.5498 - mae: 5314.5498\n",
      "Epoch 61/100\n",
      "34/34 [==============================] - 0s 11ms/step - loss: 5188.4951 - mae: 5188.4951\n",
      "Epoch 62/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 5060.2280 - mae: 5060.2280\n",
      "Epoch 63/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 4932.2085 - mae: 4932.2085\n",
      "Epoch 64/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 4801.1436 - mae: 4801.1436\n",
      "Epoch 65/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 4674.2441 - mae: 4674.2441\n",
      "Epoch 66/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 4547.1577 - mae: 4547.1577\n",
      "Epoch 67/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 4436.7588 - mae: 4436.7588\n",
      "Epoch 68/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 4328.8140 - mae: 4328.8140\n",
      "Epoch 69/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 4226.3877 - mae: 4226.3877\n",
      "Epoch 70/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 4131.8462 - mae: 4131.8462\n",
      "Epoch 71/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 4042.5083 - mae: 4042.5083\n",
      "Epoch 72/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3963.4285 - mae: 3963.4285\n",
      "Epoch 73/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3895.4966 - mae: 3895.4966\n",
      "Epoch 74/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3839.5068 - mae: 3839.5068\n",
      "Epoch 75/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3794.0630 - mae: 3794.0630\n",
      "Epoch 76/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3758.8669 - mae: 3758.8669\n",
      "Epoch 77/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3731.2339 - mae: 3731.2339\n",
      "Epoch 78/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3711.4324 - mae: 3711.4321\n",
      "Epoch 79/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3696.8850 - mae: 3696.8850\n",
      "Epoch 80/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3686.2129 - mae: 3686.2129\n",
      "Epoch 81/100\n",
      "34/34 [==============================] - 0s 9ms/step - loss: 3678.8403 - mae: 3678.8403\n",
      "Epoch 82/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3674.6846 - mae: 3674.6846\n",
      "Epoch 83/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3668.2458 - mae: 3668.2458\n",
      "Epoch 84/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3664.7656 - mae: 3664.7656\n",
      "Epoch 85/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3662.5916 - mae: 3662.5916\n",
      "Epoch 86/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3660.2590 - mae: 3660.2590\n",
      "Epoch 87/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3657.0588 - mae: 3657.0588\n",
      "Epoch 88/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3654.5447 - mae: 3654.5447\n",
      "Epoch 89/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3652.9521 - mae: 3652.9521\n",
      "Epoch 90/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3651.1331 - mae: 3651.1331\n",
      "Epoch 91/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3648.2410 - mae: 3648.2410\n",
      "Epoch 92/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3647.5010 - mae: 3647.5010\n",
      "Epoch 93/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3645.3298 - mae: 3645.3298\n",
      "Epoch 94/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3643.4866 - mae: 3643.4866\n",
      "Epoch 95/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3644.5256 - mae: 3644.5256\n",
      "Epoch 96/100\n",
      "34/34 [==============================] - 0s 8ms/step - loss: 3641.4766 - mae: 3641.4766\n",
      "Epoch 97/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3639.0518 - mae: 3639.0518\n",
      "Epoch 98/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3637.0386 - mae: 3637.0386\n",
      "Epoch 99/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3636.5947 - mae: 3636.5947\n",
      "Epoch 100/100\n",
      "34/34 [==============================] - 0s 7ms/step - loss: 3635.0735 - mae: 3635.0735\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x15023ffff40>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Build a neural network model to fit on our normalized data\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "insurance_model_4 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(100),\n",
    "    tf.keras.layers.Dense(10),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "insurance_model_4.compile(loss=tf.keras.losses.mae,\n",
    "                         optimizer = tf.keras.optimizers.Adam(),\n",
    "                         metrics=[\"mae\"])\n",
    "\n",
    "insurance_model_4.fit(X_train_normal, y_train, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "40daf1a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 8ms/step - loss: 3437.7068 - mae: 3437.7068\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3437.706787109375, 3437.706787109375]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluate model using normalized data\n",
    "insurance_model_4.evaluate(X_test_normal, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bf97905",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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