Basics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf### models\n",
    "import pandas as pd ### reading and processing data\n",
    "import seaborn as sns ### visualization\n",
    "import numpy as np### math computations\n",
    "import matplotlib.pyplot as plt### plotting bar chart\n",
    "from keras.layers import Normalization, Dense, InputLayer\n",
    "from keras.losses import MeanSquaredError, Huber, MeanAbsoluteError\n",
    "from keras.metrics import RootMeanSquaredError\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[1.000000e+00 5.356510e+05 7.981860e+05 ... 7.300000e+01 1.230000e+02\n",
      "  3.513180e+05]\n",
      " [2.000000e+00 5.919110e+05 8.610560e+05 ... 7.400000e+01 9.500000e+01\n",
      "  2.850015e+05]\n",
      " [3.000000e+00 6.869900e+05 7.707620e+05 ... 5.300000e+01 9.700000e+01\n",
      "  2.153860e+05]\n",
      " ...\n",
      " [9.980000e+02 6.463440e+05 8.427330e+05 ... 1.130000e+02 8.900000e+01\n",
      "  4.058710e+05]\n",
      " [9.990000e+02 5.355590e+05 7.324390e+05 ... 1.120000e+02 1.280000e+02\n",
      "  7.439800e+04]\n",
      " [1.000000e+03 5.901050e+05 7.797430e+05 ... 9.900000e+01 9.600000e+01\n",
      "  4.149385e+05]], shape=(1000, 12), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"train.csv\")\n",
    "tensor_data = tf.constant(data)\n",
    "tensor_data = tf.cast(tensor_data, tf.float32)\n",
    "print(tensor_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor_data = tf.random.shuffle(tensor_data)\n",
    "X = tensor_data[:,3:-1]\n",
    "y = tensor_data[:,-1]\n",
    "y = tf.expand_dims(y, axis = -1)\n",
    "TRAIN_RATIO = 0.8\n",
    "VAL_RATIO = 0.1\n",
    "TEST_RATIO = 0.1\n",
    "DATASET_SIZE = len(X)\n",
    "X_train = X[:int(DATASET_SIZE*TRAIN_RATIO)]\n",
    "y_train = y[:int(DATASET_SIZE*TRAIN_RATIO)]\n",
    "X_val = X[int(DATASET_SIZE*TRAIN_RATIO):int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO))]\n",
    "y_val = y[int(DATASET_SIZE*TRAIN_RATIO):int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO))]\n",
    "X_test = X[int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO)):]\n",
    "y_test = y[int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO)):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalizer = Normalization()\n",
    "normalizer.adapt(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " normalization_3 (Normalizat  (None, 8)                17        \n",
      " ion)                                                            \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               1152      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 128)               16512     \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 128)               16512     \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1)                 129       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 34,322\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainable params: 34,305\n",
      "Non-trainable params: 17\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential([\n",
    "                             InputLayer(input_shape = (8,)),\n",
    "                             normalizer,\n",
    "                             Dense(128, activation = \"relu\"),\n",
    "                             Dense(128, activation = \"relu\"),\n",
    "                             Dense(128, activation = \"relu\"),\n",
    "                             Dense(1),\n",
    "])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "25/25 [==============================] - 1s 7ms/step - loss: 158092.5156 - root_mean_squared_error: 203592.4531 - val_loss: 61335.9258 - val_root_mean_squared_error: 74046.4375\n",
      "Epoch 2/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 53219.8789 - root_mean_squared_error: 66203.8906 - val_loss: 50732.7891 - val_root_mean_squared_error: 62248.3008\n",
      "Epoch 3/100\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 45274.3047 - root_mean_squared_error: 56174.0586 - val_loss: 45491.1953 - val_root_mean_squared_error: 55597.0977\n",
      "Epoch 4/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 42860.3359 - root_mean_squared_error: 53374.0195 - val_loss: 45552.3984 - val_root_mean_squared_error: 55928.8672\n",
      "Epoch 5/100\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 43743.9062 - root_mean_squared_error: 54680.8203 - val_loss: 51299.8906 - val_root_mean_squared_error: 63771.0352\n",
      "Epoch 6/100\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 41207.7539 - root_mean_squared_error: 50624.3672 - val_loss: 49884.7344 - val_root_mean_squared_error: 61410.8008\n",
      "Epoch 7/100\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 42472.9805 - root_mean_squared_error: 53226.5547 - val_loss: 46918.0664 - val_root_mean_squared_error: 58375.5664\n",
      "Epoch 8/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 38516.6758 - root_mean_squared_error: 48023.8945 - val_loss: 42455.1953 - val_root_mean_squared_error: 52953.3203\n",
      "Epoch 9/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 40625.0117 - root_mean_squared_error: 50878.4102 - val_loss: 45967.4531 - val_root_mean_squared_error: 56264.6680\n",
      "Epoch 10/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 39238.2344 - root_mean_squared_error: 48503.8711 - val_loss: 48871.8633 - val_root_mean_squared_error: 60517.4062\n",
      "Epoch 11/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 37644.8984 - root_mean_squared_error: 46759.5508 - val_loss: 45623.0039 - val_root_mean_squared_error: 56106.3164\n",
      "Epoch 12/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 40327.1836 - root_mean_squared_error: 50709.1562 - val_loss: 43426.4336 - val_root_mean_squared_error: 53286.4609\n",
      "Epoch 13/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 39876.5781 - root_mean_squared_error: 48894.5664 - val_loss: 39578.5859 - val_root_mean_squared_error: 48105.6211\n",
      "Epoch 14/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 43155.9141 - root_mean_squared_error: 53308.7031 - val_loss: 40121.9766 - val_root_mean_squared_error: 49981.0117\n",
      "Epoch 15/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 46608.0469 - root_mean_squared_error: 58143.8438 - val_loss: 47104.6445 - val_root_mean_squared_error: 58109.0820\n",
      "Epoch 16/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 40256.1719 - root_mean_squared_error: 49876.0508 - val_loss: 46120.0781 - val_root_mean_squared_error: 56458.0625\n",
      "Epoch 17/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 43835.8047 - root_mean_squared_error: 55057.0273 - val_loss: 49614.3047 - val_root_mean_squared_error: 60510.7656\n",
      "Epoch 18/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 43152.5234 - root_mean_squared_error: 54240.4570 - val_loss: 45418.5781 - val_root_mean_squared_error: 55168.7070\n",
      "Epoch 19/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 38241.9727 - root_mean_squared_error: 47345.7383 - val_loss: 44321.4805 - val_root_mean_squared_error: 55234.4805\n",
      "Epoch 20/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 37762.1016 - root_mean_squared_error: 46897.7344 - val_loss: 40483.8711 - val_root_mean_squared_error: 49683.5391\n",
      "Epoch 21/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 38700.8984 - root_mean_squared_error: 48120.2617 - val_loss: 38956.2031 - val_root_mean_squared_error: 48300.3555\n",
      "Epoch 22/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36345.0000 - root_mean_squared_error: 44873.0508 - val_loss: 37777.5508 - val_root_mean_squared_error: 46793.8164\n",
      "Epoch 23/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36164.9961 - root_mean_squared_error: 45056.0586 - val_loss: 48102.7734 - val_root_mean_squared_error: 59018.6953\n",
      "Epoch 24/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 39382.2656 - root_mean_squared_error: 49341.2031 - val_loss: 44525.9609 - val_root_mean_squared_error: 53857.0625\n",
      "Epoch 25/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 39124.6289 - root_mean_squared_error: 48798.3242 - val_loss: 42167.0156 - val_root_mean_squared_error: 51759.6680\n",
      "Epoch 26/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36962.8516 - root_mean_squared_error: 45629.7070 - val_loss: 42750.9492 - val_root_mean_squared_error: 52799.2188\n",
      "Epoch 27/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 37029.6914 - root_mean_squared_error: 46226.7070 - val_loss: 42233.0586 - val_root_mean_squared_error: 50906.1133\n",
      "Epoch 28/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35099.4688 - root_mean_squared_error: 43506.8281 - val_loss: 42321.1992 - val_root_mean_squared_error: 51665.9531\n",
      "Epoch 29/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35566.6758 - root_mean_squared_error: 44472.8828 - val_loss: 40020.5117 - val_root_mean_squared_error: 50064.6094\n",
      "Epoch 30/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 37128.6758 - root_mean_squared_error: 45860.7422 - val_loss: 40002.0312 - val_root_mean_squared_error: 49519.2539\n",
      "Epoch 31/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 41663.4648 - root_mean_squared_error: 51897.7188 - val_loss: 43999.4883 - val_root_mean_squared_error: 53862.9336\n",
      "Epoch 32/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36621.6211 - root_mean_squared_error: 45800.3242 - val_loss: 45061.5469 - val_root_mean_squared_error: 54144.0078\n",
      "Epoch 33/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 40328.0234 - root_mean_squared_error: 50349.1992 - val_loss: 39665.8594 - val_root_mean_squared_error: 48036.3555\n",
      "Epoch 34/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 42082.4609 - root_mean_squared_error: 52971.0039 - val_loss: 41785.7656 - val_root_mean_squared_error: 50305.7812\n",
      "Epoch 35/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 41619.3320 - root_mean_squared_error: 52606.9102 - val_loss: 43059.3359 - val_root_mean_squared_error: 53173.7500\n",
      "Epoch 36/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36105.0586 - root_mean_squared_error: 45191.5938 - val_loss: 38500.0469 - val_root_mean_squared_error: 47024.5156\n",
      "Epoch 37/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35490.6719 - root_mean_squared_error: 44173.3086 - val_loss: 39252.1523 - val_root_mean_squared_error: 48638.1562\n",
      "Epoch 38/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35511.7734 - root_mean_squared_error: 43863.6992 - val_loss: 49590.4766 - val_root_mean_squared_error: 61664.7227\n",
      "Epoch 39/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 37499.4492 - root_mean_squared_error: 46792.4688 - val_loss: 42278.9609 - val_root_mean_squared_error: 53054.7617\n",
      "Epoch 40/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35451.2070 - root_mean_squared_error: 44164.8555 - val_loss: 38880.2539 - val_root_mean_squared_error: 48001.3242\n",
      "Epoch 41/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35215.9375 - root_mean_squared_error: 43577.5977 - val_loss: 39024.5352 - val_root_mean_squared_error: 46941.2109\n",
      "Epoch 42/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36075.0977 - root_mean_squared_error: 44715.6250 - val_loss: 39498.2773 - val_root_mean_squared_error: 49024.6094\n",
      "Epoch 43/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 42799.2148 - root_mean_squared_error: 53443.1719 - val_loss: 37872.7812 - val_root_mean_squared_error: 45935.5117\n",
      "Epoch 44/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 38301.9844 - root_mean_squared_error: 47980.4570 - val_loss: 50124.2852 - val_root_mean_squared_error: 60572.7734\n",
      "Epoch 45/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 38235.2266 - root_mean_squared_error: 47937.1367 - val_loss: 42516.6016 - val_root_mean_squared_error: 54495.0039\n",
      "Epoch 46/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36853.3086 - root_mean_squared_error: 45943.2500 - val_loss: 42791.2383 - val_root_mean_squared_error: 52048.3867\n",
      "Epoch 47/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 40540.0938 - root_mean_squared_error: 50378.7500 - val_loss: 39972.8320 - val_root_mean_squared_error: 49128.5469\n",
      "Epoch 48/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36871.9375 - root_mean_squared_error: 46498.0508 - val_loss: 41941.3789 - val_root_mean_squared_error: 50999.3633\n",
      "Epoch 49/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34927.2344 - root_mean_squared_error: 43293.8359 - val_loss: 41769.2617 - val_root_mean_squared_error: 50990.5977\n",
      "Epoch 50/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 37835.6289 - root_mean_squared_error: 47465.2109 - val_loss: 49514.6133 - val_root_mean_squared_error: 60592.1094\n",
      "Epoch 51/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36376.8906 - root_mean_squared_error: 45272.0000 - val_loss: 45910.0000 - val_root_mean_squared_error: 57873.5977\n",
      "Epoch 52/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 42049.1641 - root_mean_squared_error: 52947.8672 - val_loss: 38517.1484 - val_root_mean_squared_error: 47012.7617\n",
      "Epoch 53/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 38402.0859 - root_mean_squared_error: 48518.8711 - val_loss: 46542.2148 - val_root_mean_squared_error: 56621.5078\n",
      "Epoch 54/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 45936.1445 - root_mean_squared_error: 57519.4766 - val_loss: 59231.3086 - val_root_mean_squared_error: 70586.2969\n",
      "Epoch 55/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 43860.0000 - root_mean_squared_error: 55220.7930 - val_loss: 57122.0508 - val_root_mean_squared_error: 68541.9844\n",
      "Epoch 56/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36691.6523 - root_mean_squared_error: 45386.0547 - val_loss: 38399.1289 - val_root_mean_squared_error: 46648.2695\n",
      "Epoch 57/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35262.9102 - root_mean_squared_error: 44255.1602 - val_loss: 49730.8750 - val_root_mean_squared_error: 59721.6211\n",
      "Epoch 58/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35581.3984 - root_mean_squared_error: 44265.6367 - val_loss: 39533.8789 - val_root_mean_squared_error: 47890.1445\n",
      "Epoch 59/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36514.2109 - root_mean_squared_error: 45524.3945 - val_loss: 40070.3711 - val_root_mean_squared_error: 49779.3594\n",
      "Epoch 60/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36472.6016 - root_mean_squared_error: 44887.4570 - val_loss: 40249.8984 - val_root_mean_squared_error: 50117.0977\n",
      "Epoch 61/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 37498.3359 - root_mean_squared_error: 47189.3867 - val_loss: 43859.4062 - val_root_mean_squared_error: 54247.8555\n",
      "Epoch 62/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34998.8984 - root_mean_squared_error: 44026.6094 - val_loss: 43859.7969 - val_root_mean_squared_error: 54138.1016\n",
      "Epoch 63/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35311.6953 - root_mean_squared_error: 44097.2266 - val_loss: 39984.4180 - val_root_mean_squared_error: 50410.9922\n",
      "Epoch 64/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34162.1367 - root_mean_squared_error: 42835.3750 - val_loss: 38843.6914 - val_root_mean_squared_error: 47446.6758\n",
      "Epoch 65/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35101.5078 - root_mean_squared_error: 43086.2070 - val_loss: 39579.5430 - val_root_mean_squared_error: 48366.1797\n",
      "Epoch 66/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34136.9688 - root_mean_squared_error: 42841.0391 - val_loss: 40894.3242 - val_root_mean_squared_error: 49694.6172\n",
      "Epoch 67/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33595.9336 - root_mean_squared_error: 42015.9297 - val_loss: 41590.0039 - val_root_mean_squared_error: 51279.1484\n",
      "Epoch 68/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33788.1055 - root_mean_squared_error: 42484.5195 - val_loss: 47969.3789 - val_root_mean_squared_error: 58366.8555\n",
      "Epoch 69/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34043.4219 - root_mean_squared_error: 42694.5781 - val_loss: 40279.4336 - val_root_mean_squared_error: 49208.0391\n",
      "Epoch 70/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35571.9180 - root_mean_squared_error: 44361.6992 - val_loss: 44222.1367 - val_root_mean_squared_error: 55289.5000\n",
      "Epoch 71/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34000.9102 - root_mean_squared_error: 42430.1719 - val_loss: 39260.6133 - val_root_mean_squared_error: 48147.7305\n",
      "Epoch 72/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33917.8281 - root_mean_squared_error: 42354.0039 - val_loss: 46515.8711 - val_root_mean_squared_error: 56967.2656\n",
      "Epoch 73/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34284.9766 - root_mean_squared_error: 42939.8672 - val_loss: 44814.4961 - val_root_mean_squared_error: 55646.0273\n",
      "Epoch 74/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35314.7266 - root_mean_squared_error: 43867.9883 - val_loss: 45503.4609 - val_root_mean_squared_error: 54613.8867\n",
      "Epoch 75/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 38990.3984 - root_mean_squared_error: 48306.7734 - val_loss: 49273.6602 - val_root_mean_squared_error: 60735.3047\n",
      "Epoch 76/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33957.2656 - root_mean_squared_error: 42761.2109 - val_loss: 47606.8281 - val_root_mean_squared_error: 57817.2227\n",
      "Epoch 77/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34441.8789 - root_mean_squared_error: 42850.6680 - val_loss: 45808.4961 - val_root_mean_squared_error: 56308.4336\n",
      "Epoch 78/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33217.1719 - root_mean_squared_error: 41852.8750 - val_loss: 43194.3711 - val_root_mean_squared_error: 52643.4141\n",
      "Epoch 79/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36101.0156 - root_mean_squared_error: 45489.7148 - val_loss: 43111.1641 - val_root_mean_squared_error: 52176.7344\n",
      "Epoch 80/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 38043.4219 - root_mean_squared_error: 47093.4766 - val_loss: 46462.1055 - val_root_mean_squared_error: 56679.7812\n",
      "Epoch 81/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36885.0742 - root_mean_squared_error: 45879.3711 - val_loss: 42177.8516 - val_root_mean_squared_error: 50888.4453\n",
      "Epoch 82/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33991.5547 - root_mean_squared_error: 42400.8281 - val_loss: 41674.3711 - val_root_mean_squared_error: 51545.2461\n",
      "Epoch 83/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 39228.6484 - root_mean_squared_error: 48100.3867 - val_loss: 46937.5117 - val_root_mean_squared_error: 58789.9258\n",
      "Epoch 84/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 32742.2480 - root_mean_squared_error: 40650.3086 - val_loss: 40165.9141 - val_root_mean_squared_error: 48070.4648\n",
      "Epoch 85/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33292.3984 - root_mean_squared_error: 41505.3750 - val_loss: 40083.6875 - val_root_mean_squared_error: 48446.0391\n",
      "Epoch 86/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34474.2227 - root_mean_squared_error: 42820.5586 - val_loss: 40604.0195 - val_root_mean_squared_error: 48307.7695\n",
      "Epoch 87/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33049.2109 - root_mean_squared_error: 41675.6914 - val_loss: 38789.3047 - val_root_mean_squared_error: 48130.0547\n",
      "Epoch 88/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33539.1016 - root_mean_squared_error: 41967.7812 - val_loss: 41829.3789 - val_root_mean_squared_error: 51781.0078\n",
      "Epoch 89/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33692.4570 - root_mean_squared_error: 42470.5547 - val_loss: 42951.4219 - val_root_mean_squared_error: 52364.5664\n",
      "Epoch 90/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33706.4023 - root_mean_squared_error: 42139.1758 - val_loss: 41572.8477 - val_root_mean_squared_error: 50512.5938\n",
      "Epoch 91/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34616.5508 - root_mean_squared_error: 43375.7070 - val_loss: 41847.7383 - val_root_mean_squared_error: 49358.0820\n",
      "Epoch 92/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34979.0820 - root_mean_squared_error: 43953.5547 - val_loss: 46466.0117 - val_root_mean_squared_error: 56935.2461\n",
      "Epoch 93/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 35202.3906 - root_mean_squared_error: 44673.0820 - val_loss: 42755.5117 - val_root_mean_squared_error: 51029.9648\n",
      "Epoch 94/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36065.9375 - root_mean_squared_error: 45688.7109 - val_loss: 41589.8516 - val_root_mean_squared_error: 49351.0039\n",
      "Epoch 95/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 36833.4688 - root_mean_squared_error: 46123.6719 - val_loss: 40297.3359 - val_root_mean_squared_error: 49689.7422\n",
      "Epoch 96/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 32504.6582 - root_mean_squared_error: 40495.3945 - val_loss: 42400.8047 - val_root_mean_squared_error: 53128.7812\n",
      "Epoch 97/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 32473.3184 - root_mean_squared_error: 40767.1133 - val_loss: 40888.3086 - val_root_mean_squared_error: 49536.8086\n",
      "Epoch 98/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34368.2539 - root_mean_squared_error: 43002.2891 - val_loss: 40436.9531 - val_root_mean_squared_error: 49904.7188\n",
      "Epoch 99/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 34060.9336 - root_mean_squared_error: 42742.5430 - val_loss: 42645.3008 - val_root_mean_squared_error: 51390.1992\n",
      "Epoch 100/100\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 33040.4961 - root_mean_squared_error: 41428.3594 - val_loss: 41086.3477 - val_root_mean_squared_error: 51047.7070\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer = Adam(learning_rate = 0.1),\n",
    "              loss = MeanAbsoluteError(),\n",
    "              metrics = RootMeanSquaredError())\n",
    "history=model.fit(X_train,y_train, validation_data=(X_val,y_val),epochs=100, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val_loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 60ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[395674.47 ],\n",
       "        [354272.34 ],\n",
       "        [429016.78 ],\n",
       "        [445700.6  ],\n",
       "        [522430.03 ],\n",
       "        [413651.6  ],\n",
       "        [156941.4  ],\n",
       "        [146537.22 ],\n",
       "        [421767.6  ],\n",
       "        [200799.45 ],\n",
       "        [153247.52 ],\n",
       "        [162187.08 ],\n",
       "        [472853.34 ],\n",
       "        [479488.56 ],\n",
       "        [128254.46 ],\n",
       "        [191577.08 ],\n",
       "        [164506.19 ],\n",
       "        [335408.5  ],\n",
       "        [446453.97 ],\n",
       "        [313963.6  ],\n",
       "        [530248.4  ],\n",
       "        [178014.05 ],\n",
       "        [121700.74 ],\n",
       "        [389252.66 ],\n",
       "        [336017.94 ],\n",
       "        [124346.   ],\n",
       "        [483585.28 ],\n",
       "        [429199.72 ],\n",
       "        [467280.75 ],\n",
       "        [157122.34 ],\n",
       "        [483908.62 ],\n",
       "        [407805.75 ],\n",
       "        [195225.8  ],\n",
       "        [524693.   ],\n",
       "        [234729.47 ],\n",
       "        [134459.36 ],\n",
       "        [375241.66 ],\n",
       "        [251698.25 ],\n",
       "        [165900.14 ],\n",
       "        [248469.44 ],\n",
       "        [135554.47 ],\n",
       "        [457937.25 ],\n",
       "        [203323.44 ],\n",
       "        [319791.1  ],\n",
       "        [513813.03 ],\n",
       "        [172078.28 ],\n",
       "        [388761.62 ],\n",
       "        [162354.12 ],\n",
       "        [222825.62 ],\n",
       "        [278646.06 ],\n",
       "        [366529.66 ],\n",
       "        [285097.28 ],\n",
       "        [352828.62 ],\n",
       "        [251455.03 ],\n",
       "        [454501.25 ],\n",
       "        [223164.75 ],\n",
       "        [417272.84 ],\n",
       "        [206314.61 ],\n",
       "        [ 93152.445],\n",
       "        [105920.82 ],\n",
       "        [323711.5  ],\n",
       "        [304984.16 ],\n",
       "        [441979.56 ],\n",
       "        [346667.16 ],\n",
       "        [203755.62 ],\n",
       "        [442590.34 ],\n",
       "        [203921.11 ],\n",
       "        [391233.72 ],\n",
       "        [181867.14 ],\n",
       "        [135567.38 ],\n",
       "        [455158.72 ],\n",
       "        [207243.64 ],\n",
       "        [223245.22 ],\n",
       "        [141825.1  ],\n",
       "        [202952.92 ],\n",
       "        [407340.34 ],\n",
       "        [427447.97 ],\n",
       "        [210089.84 ],\n",
       "        [234978.84 ],\n",
       "        [182508.27 ],\n",
       "        [398980.3  ],\n",
       "        [304615.28 ],\n",
       "        [517755.84 ],\n",
       "        [207459.58 ],\n",
       "        [331109.78 ],\n",
       "        [198453.16 ],\n",
       "        [219155.88 ],\n",
       "        [328418.62 ],\n",
       "        [330514.72 ],\n",
       "        [206455.19 ],\n",
       "        [155015.25 ],\n",
       "        [457630.47 ],\n",
       "        [408338.44 ],\n",
       "        [276730.56 ],\n",
       "        [440813.   ],\n",
       "        [176701.98 ],\n",
       "        [329346.1  ],\n",
       "        [162080.53 ],\n",
       "        [328334.34 ],\n",
       "        [344781.6  ]]], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(tf.expand_dims(X_test, axis = 0 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(100, 1), dtype=float32, numpy=\n",
       "array([[467395. ],\n",
       "       [269326. ],\n",
       "       [381846.5],\n",
       "       [506854. ],\n",
       "       [515454. ],\n",
       "       [435007.5],\n",
       "       [149899.5],\n",
       "       [132026.5],\n",
       "       [367734.5],\n",
       "       [121022. ],\n",
       "       [131893.5],\n",
       "       [207352.5],\n",
       "       [494575.5],\n",
       "       [501920.5],\n",
       "       [177933.5],\n",
       "       [204833.5],\n",
       "       [112864.5],\n",
       "       [299019.5],\n",
       "       [488457. ],\n",
       "       [329538.5],\n",
       "       [468543. ],\n",
       "       [140884. ],\n",
       "       [ 77167. ],\n",
       "       [371227. ],\n",
       "       [328863. ],\n",
       "       [150076. ],\n",
       "       [412014.5],\n",
       "       [471527.5],\n",
       "       [458591.5],\n",
       "       [157507. ],\n",
       "       [390756.5],\n",
       "       [431060. ],\n",
       "       [187009. ],\n",
       "       [460780. ],\n",
       "       [239815.5],\n",
       "       [ 95533.5],\n",
       "       [380289.5],\n",
       "       [193067. ],\n",
       "       [152125.5],\n",
       "       [212553.5],\n",
       "       [151474. ],\n",
       "       [465338.5],\n",
       "       [220518. ],\n",
       "       [340112. ],\n",
       "       [559108. ],\n",
       "       [210661.5],\n",
       "       [327994. ],\n",
       "       [ 96554. ],\n",
       "       [177509.5],\n",
       "       [221692. ],\n",
       "       [354900. ],\n",
       "       [291031.5],\n",
       "       [422774.5],\n",
       "       [284727.5],\n",
       "       [433504. ],\n",
       "       [188131.5],\n",
       "       [425777.5],\n",
       "       [253530.5],\n",
       "       [136824.5],\n",
       "       [152879.5],\n",
       "       [308867. ],\n",
       "       [294378.5],\n",
       "       [426796.5],\n",
       "       [389512. ],\n",
       "       [203811.5],\n",
       "       [425960. ],\n",
       "       [250955. ],\n",
       "       [411701. ],\n",
       "       [230428.5],\n",
       "       [172234.5],\n",
       "       [363186.5],\n",
       "       [175012.5],\n",
       "       [190956.5],\n",
       "       [151080.5],\n",
       "       [202883.5],\n",
       "       [462499. ],\n",
       "       [496514. ],\n",
       "       [213768.5],\n",
       "       [158031.5],\n",
       "       [110359.5],\n",
       "       [399672. ],\n",
       "       [340720. ],\n",
       "       [553855. ],\n",
       "       [239018. ],\n",
       "       [288926. ],\n",
       "       [245796. ],\n",
       "       [275045.5],\n",
       "       [316652. ],\n",
       "       [285487. ],\n",
       "       [219127. ],\n",
       "       [ 87234.5],\n",
       "       [426760. ],\n",
       "       [307993. ],\n",
       "       [272816.5],\n",
       "       [408860.5],\n",
       "       [229110. ],\n",
       "       [308643.5],\n",
       "       [192853.5],\n",
       "       [282496.5],\n",
       "       [340777. ]], dtype=float32)>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 4000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_true = list(y_test[:,0].numpy())\n",
    "y_pred = list(model.predict(X_test)[:,0])\n",
    "ind = np.arange(100)\n",
    "plt.figure(figsize=(40,20))\n",
    "\n",
    "width = 0.1\n",
    "\n",
    "plt.bar(ind, y_pred, width, label='Predicted Car Price')\n",
    "plt.bar(ind + width, y_true, width, label='Actual Car Price')\n",
    "\n",
    "plt.xlabel('Actual vs Predicted Prices')\n",
    "plt.ylabel('Car Price Prices')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}