basic_regression_fuel_efficiency.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "np.set_printoptions(precision=3, suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.3\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers.experimental import preprocessing\n",
    "\n",
    "print(tf.__version__)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'\n",
    "column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n",
    "                'Acceleration', 'Model Year', 'Origin']\n",
    "raw_dataset = pd.read_csv(url, names=column_names,\n",
    "                          na_values='?', comment='\\t',\n",
    "                          sep=' ', skipinitialspace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n393  27.0          4         140.0        86.0  2790.0          15.6   \n394  44.0          4          97.0        52.0  2130.0          24.6   \n395  32.0          4         135.0        84.0  2295.0          11.6   \n396  28.0          4         120.0        79.0  2625.0          18.6   \n397  31.0          4         119.0        82.0  2720.0          19.4   \n\n     Model Year  Origin  \n393          82       1  \n394          82       2  \n395          82       1  \n396          82       1  \n397          82       1  ",
      "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>MPG</th>\n      <th>Cylinders</th>\n      <th>Displacement</th>\n      <th>Horsepower</th>\n      <th>Weight</th>\n      <th>Acceleration</th>\n      <th>Model Year</th>\n      <th>Origin</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>393</th>\n      <td>27.0</td>\n      <td>4</td>\n      <td>140.0</td>\n      <td>86.0</td>\n      <td>2790.0</td>\n      <td>15.6</td>\n      <td>82</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>394</th>\n      <td>44.0</td>\n      <td>4</td>\n      <td>97.0</td>\n      <td>52.0</td>\n      <td>2130.0</td>\n      <td>24.6</td>\n      <td>82</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>395</th>\n      <td>32.0</td>\n      <td>4</td>\n      <td>135.0</td>\n      <td>84.0</td>\n      <td>2295.0</td>\n      <td>11.6</td>\n      <td>82</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>396</th>\n      <td>28.0</td>\n      <td>4</td>\n      <td>120.0</td>\n      <td>79.0</td>\n      <td>2625.0</td>\n      <td>18.6</td>\n      <td>82</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>397</th>\n      <td>31.0</td>\n      <td>4</td>\n      <td>119.0</td>\n      <td>82.0</td>\n      <td>2720.0</td>\n      <td>19.4</td>\n      <td>82</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = raw_dataset.copy()\n",
    "dataset.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "MPG             0\nCylinders       0\nDisplacement    0\nHorsepower      6\nWeight          0\nAcceleration    0\nModel Year      0\nOrigin          0\ndtype: int64"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n393  27.0          4         140.0        86.0  2790.0          15.6   \n394  44.0          4          97.0        52.0  2130.0          24.6   \n395  32.0          4         135.0        84.0  2295.0          11.6   \n396  28.0          4         120.0        79.0  2625.0          18.6   \n397  31.0          4         119.0        82.0  2720.0          19.4   \n\n     Model Year  Origin  \n393          82     USA  \n394          82  Europe  \n395          82     USA  \n396          82     USA  \n397          82     USA  ",
      "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>MPG</th>\n      <th>Cylinders</th>\n      <th>Displacement</th>\n      <th>Horsepower</th>\n      <th>Weight</th>\n      <th>Acceleration</th>\n      <th>Model Year</th>\n      <th>Origin</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>393</th>\n      <td>27.0</td>\n      <td>4</td>\n      <td>140.0</td>\n      <td>86.0</td>\n      <td>2790.0</td>\n      <td>15.6</td>\n      <td>82</td>\n      <td>USA</td>\n    </tr>\n    <tr>\n      <th>394</th>\n      <td>44.0</td>\n      <td>4</td>\n      <td>97.0</td>\n      <td>52.0</td>\n      <td>2130.0</td>\n      <td>24.6</td>\n      <td>82</td>\n      <td>Europe</td>\n    </tr>\n    <tr>\n      <th>395</th>\n      <td>32.0</td>\n      <td>4</td>\n      <td>135.0</td>\n      <td>84.0</td>\n      <td>2295.0</td>\n      <td>11.6</td>\n      <td>82</td>\n      <td>USA</td>\n    </tr>\n    <tr>\n      <th>396</th>\n      <td>28.0</td>\n      <td>4</td>\n      <td>120.0</td>\n      <td>79.0</td>\n      <td>2625.0</td>\n      <td>18.6</td>\n      <td>82</td>\n      <td>USA</td>\n    </tr>\n    <tr>\n      <th>397</th>\n      <td>31.0</td>\n      <td>4</td>\n      <td>119.0</td>\n      <td>82.0</td>\n      <td>2720.0</td>\n      <td>19.4</td>\n      <td>82</td>\n      <td>USA</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n393  27.0          4         140.0        86.0  2790.0          15.6   \n394  44.0          4          97.0        52.0  2130.0          24.6   \n395  32.0          4         135.0        84.0  2295.0          11.6   \n396  28.0          4         120.0        79.0  2625.0          18.6   \n397  31.0          4         119.0        82.0  2720.0          19.4   \n\n     Model Year  Europe  Japan  USA  \n393          82       0      0    1  \n394          82       1      0    0  \n395          82       0      0    1  \n396          82       0      0    1  \n397          82       0      0    1  ",
      "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>MPG</th>\n      <th>Cylinders</th>\n      <th>Displacement</th>\n      <th>Horsepower</th>\n      <th>Weight</th>\n      <th>Acceleration</th>\n      <th>Model Year</th>\n      <th>Europe</th>\n      <th>Japan</th>\n      <th>USA</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>393</th>\n      <td>27.0</td>\n      <td>4</td>\n      <td>140.0</td>\n      <td>86.0</td>\n      <td>2790.0</td>\n      <td>15.6</td>\n      <td>82</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>394</th>\n      <td>44.0</td>\n      <td>4</td>\n      <td>97.0</td>\n      <td>52.0</td>\n      <td>2130.0</td>\n      <td>24.6</td>\n      <td>82</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>395</th>\n      <td>32.0</td>\n      <td>4</td>\n      <td>135.0</td>\n      <td>84.0</td>\n      <td>2295.0</td>\n      <td>11.6</td>\n      <td>82</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>396</th>\n      <td>28.0</td>\n      <td>4</td>\n      <td>120.0</td>\n      <td>79.0</td>\n      <td>2625.0</td>\n      <td>18.6</td>\n      <td>82</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>397</th>\n      <td>31.0</td>\n      <td>4</td>\n      <td>119.0</td>\n      <td>82.0</td>\n      <td>2720.0</td>\n      <td>19.4</td>\n      <td>82</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.get_dummies(dataset, columns=['Origin'], prefix='', prefix_sep='')\n",
    "dataset.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "train_dataset = dataset.sample(frac=0.8, random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.PairGrid at 0x7f3ea70b1a58>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 720x720 with 20 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "              count         mean         std     min      25%     50%  \\\nMPG           314.0    23.310510    7.728652    10.0    17.00    22.0   \nCylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \nDisplacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \nHorsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \nWeight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \nAcceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \nModel Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \nEurope        314.0     0.178344    0.383413     0.0     0.00     0.0   \nJapan         314.0     0.197452    0.398712     0.0     0.00     0.0   \nUSA           314.0     0.624204    0.485101     0.0     0.00     1.0   \n\n                  75%     max  \nMPG             28.95    46.6  \nCylinders        8.00     8.0  \nDisplacement   265.75   455.0  \nHorsepower     128.00   225.0  \nWeight        3608.00  5140.0  \nAcceleration    17.20    24.8  \nModel Year      79.00    82.0  \nEurope           0.00     1.0  \nJapan            0.00     1.0  \nUSA              1.00     1.0  ",
      "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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MPG</th>\n      <td>314.0</td>\n      <td>23.310510</td>\n      <td>7.728652</td>\n      <td>10.0</td>\n      <td>17.00</td>\n      <td>22.0</td>\n      <td>28.95</td>\n      <td>46.6</td>\n    </tr>\n    <tr>\n      <th>Cylinders</th>\n      <td>314.0</td>\n      <td>5.477707</td>\n      <td>1.699788</td>\n      <td>3.0</td>\n      <td>4.00</td>\n      <td>4.0</td>\n      <td>8.00</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>Displacement</th>\n      <td>314.0</td>\n      <td>195.318471</td>\n      <td>104.331589</td>\n      <td>68.0</td>\n      <td>105.50</td>\n      <td>151.0</td>\n      <td>265.75</td>\n      <td>455.0</td>\n    </tr>\n    <tr>\n      <th>Horsepower</th>\n      <td>314.0</td>\n      <td>104.869427</td>\n      <td>38.096214</td>\n      <td>46.0</td>\n      <td>76.25</td>\n      <td>94.5</td>\n      <td>128.00</td>\n      <td>225.0</td>\n    </tr>\n    <tr>\n      <th>Weight</th>\n      <td>314.0</td>\n      <td>2990.251592</td>\n      <td>843.898596</td>\n      <td>1649.0</td>\n      <td>2256.50</td>\n      <td>2822.5</td>\n      <td>3608.00</td>\n      <td>5140.0</td>\n    </tr>\n    <tr>\n      <th>Acceleration</th>\n      <td>314.0</td>\n      <td>15.559236</td>\n      <td>2.789230</td>\n      <td>8.0</td>\n      <td>13.80</td>\n      <td>15.5</td>\n      <td>17.20</td>\n      <td>24.8</td>\n    </tr>\n    <tr>\n      <th>Model Year</th>\n      <td>314.0</td>\n      <td>75.898089</td>\n      <td>3.675642</td>\n      <td>70.0</td>\n      <td>73.00</td>\n      <td>76.0</td>\n      <td>79.00</td>\n      <td>82.0</td>\n    </tr>\n    <tr>\n      <th>Europe</th>\n      <td>314.0</td>\n      <td>0.178344</td>\n      <td>0.383413</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Japan</th>\n      <td>314.0</td>\n      <td>0.197452</td>\n      <td>0.398712</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>USA</th>\n      <td>314.0</td>\n      <td>0.624204</td>\n      <td>0.485101</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>1.0</td>\n      <td>1.00</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.describe().transpose()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "train_features = train_dataset.copy()\n",
    "test_features = test_dataset.copy()\n",
    "\n",
    "train_labels = train_features.pop('MPG')\n",
    "test_labels = test_features.pop('MPG')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "                     mean         std\nMPG             23.310510    7.728652\nCylinders        5.477707    1.699788\nDisplacement   195.318471  104.331589\nHorsepower     104.869427   38.096214\nWeight        2990.251592  843.898596\nAcceleration    15.559236    2.789230\nModel Year      75.898089    3.675642\nEurope           0.178344    0.383413\nJapan            0.197452    0.398712\nUSA              0.624204    0.485101",
      "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>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MPG</th>\n      <td>23.310510</td>\n      <td>7.728652</td>\n    </tr>\n    <tr>\n      <th>Cylinders</th>\n      <td>5.477707</td>\n      <td>1.699788</td>\n    </tr>\n    <tr>\n      <th>Displacement</th>\n      <td>195.318471</td>\n      <td>104.331589</td>\n    </tr>\n    <tr>\n      <th>Horsepower</th>\n      <td>104.869427</td>\n      <td>38.096214</td>\n    </tr>\n    <tr>\n      <th>Weight</th>\n      <td>2990.251592</td>\n      <td>843.898596</td>\n    </tr>\n    <tr>\n      <th>Acceleration</th>\n      <td>15.559236</td>\n      <td>2.789230</td>\n    </tr>\n    <tr>\n      <th>Model Year</th>\n      <td>75.898089</td>\n      <td>3.675642</td>\n    </tr>\n    <tr>\n      <th>Europe</th>\n      <td>0.178344</td>\n      <td>0.383413</td>\n    </tr>\n    <tr>\n      <th>Japan</th>\n      <td>0.197452</td>\n      <td>0.398712</td>\n    </tr>\n    <tr>\n      <th>USA</th>\n      <td>0.624204</td>\n      <td>0.485101</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.describe().transpose()[['mean', 'std']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "     Cylinders  Displacement  Horsepower  Weight  Acceleration  Model Year  \\\n281          6         200.0        85.0  2990.0          18.2          79   \n229          8         400.0       180.0  4220.0          11.1          77   \n150          4         108.0        93.0  2391.0          15.5          74   \n145          4          83.0        61.0  2003.0          19.0          74   \n182          4         107.0        86.0  2464.0          15.5          76   \n\n     Europe  Japan  USA  \n281       0      0    1  \n229       0      0    1  \n150       0      1    0  \n145       0      1    0  \n182       1      0    0  ",
      "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>Cylinders</th>\n      <th>Displacement</th>\n      <th>Horsepower</th>\n      <th>Weight</th>\n      <th>Acceleration</th>\n      <th>Model Year</th>\n      <th>Europe</th>\n      <th>Japan</th>\n      <th>USA</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>281</th>\n      <td>6</td>\n      <td>200.0</td>\n      <td>85.0</td>\n      <td>2990.0</td>\n      <td>18.2</td>\n      <td>79</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>229</th>\n      <td>8</td>\n      <td>400.0</td>\n      <td>180.0</td>\n      <td>4220.0</td>\n      <td>11.1</td>\n      <td>77</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>150</th>\n      <td>4</td>\n      <td>108.0</td>\n      <td>93.0</td>\n      <td>2391.0</td>\n      <td>15.5</td>\n      <td>74</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>4</td>\n      <td>83.0</td>\n      <td>61.0</td>\n      <td>2003.0</td>\n      <td>19.0</td>\n      <td>74</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>182</th>\n      <td>4</td>\n      <td>107.0</td>\n      <td>86.0</td>\n      <td>2464.0</td>\n      <td>15.5</td>\n      <td>76</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "281    19.8\n229    16.0\n150    26.0\n145    32.0\n182    28.0\nName: MPG, dtype: float64"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "normalizer = preprocessing.Normalization(axis=-1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "normalizer.adapt(np.array(train_features))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   5.478  195.318  104.869 2990.252   15.559   75.898    0.178    0.197\n",
      "    0.624]\n"
     ]
    }
   ],
   "source": [
    "print(normalizer.mean.numpy())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First example:  [[   4.    90.    75.  2125.    14.5   74.     0.     0.     1. ]]\n",
      "\n",
      "Normalized:  [[-0.87 -1.01 -0.79 -1.03 -0.38 -0.52 -0.47 -0.5   0.78]]\n"
     ]
    }
   ],
   "source": [
    "first = np.array(train_features[:1])\n",
    "\n",
    "with np.printoptions(precision = 2, suppress=True):\n",
    "    print(\"First example: \", first)\n",
    "    print()\n",
    "    print(\"Normalized: \", normalizer(first).numpy())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "horsepower = np.array(train_features['Horsepower'])\n",
    "horsepower_normalizer = preprocessing.Normalization(input_shape=[1, ], axis=None)\n",
    "horsepower_normalizer.adapt(horsepower)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "normalization_1 (Normalizati (None, 1)                 3         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 5\n",
      "Trainable params: 2\n",
      "Non-trainable params: 3\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "horsepower_model = tf.keras.Sequential([\n",
    "    horsepower_normalizer,\n",
    "    layers.Dense(units=1)\n",
    "])\n",
    "\n",
    "horsepower_model.summary()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0.322],\n       [ 0.182],\n       [-0.593],\n       [ 0.451],\n       [ 0.408],\n       [ 0.16 ],\n       [ 0.483],\n       [ 0.408],\n       [ 0.106],\n       [ 0.182]], dtype=float32)"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horsepower_model.predict(horsepower[:10])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "horsepower_model.compile(\n",
    "    optimizer=tf.optimizers.Adam(learning_rate=0.1),\n",
    "    loss = 'mean_absolute_error'\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.42 s, sys: 230 ms, total: 2.65 s\n",
      "Wall time: 1.84 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = horsepower_model.fit(\n",
    "    train_features['Horsepower'], train_labels,\n",
    "    epochs=100,\n",
    "    verbose=0,\n",
    "    validation_split=0.2\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "        loss  val_loss  epoch\n95  3.802618  4.204325     95\n96  3.802436  4.195175     96\n97  3.804209  4.173783     97\n98  3.804647  4.166675     98\n99  3.809342  4.189342     99",
      "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>loss</th>\n      <th>val_loss</th>\n      <th>epoch</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>95</th>\n      <td>3.802618</td>\n      <td>4.204325</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>3.802436</td>\n      <td>4.195175</td>\n      <td>96</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>3.804209</td>\n      <td>4.173783</td>\n      <td>97</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>3.804647</td>\n      <td>4.166675</td>\n      <td>98</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>3.809342</td>\n      <td>4.189342</td>\n      <td>99</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "def plot_loss(history):\n",
    "  plt.plot(history.history['loss'], label='loss')\n",
    "  plt.plot(history.history['val_loss'], label='val_loss')\n",
    "  plt.ylim([0, 10])\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Error [MPG]')\n",
    "  plt.legend()\n",
    "  plt.grid(True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "test_results = {}\n",
    "\n",
    "test_results['horsepower_model'] = horsepower_model.evaluate(\n",
    "    test_features['Horsepower'],\n",
    "    test_labels, verbose=0\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "x = tf.linspace(0.0, 250, 251)\n",
    "y = horsepower_model.predict(x)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "def plot_horsepower(x, y):\n",
    "  plt.scatter(train_features['Horsepower'], train_labels, label='Data')\n",
    "  plt.plot(x, y, color='k', label='Predictions')\n",
    "  plt.xlabel('Horsepower')\n",
    "  plt.ylabel('MPG')\n",
    "  plt.legend()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_horsepower(x,y)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "linear_model = tf.keras.Sequential([\n",
    "    normalizer,\n",
    "    layers.Dense(units=1)\n",
    "])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 1.667],\n       [-0.103],\n       [-0.218],\n       [ 0.666],\n       [ 0.365],\n       [-0.462],\n       [ 0.298],\n       [-2.992],\n       [ 0.325],\n       [ 0.776]], dtype=float32)"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.predict(train_features[:10])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "<tf.Variable 'dense_1/kernel:0' shape=(9, 1) dtype=float32, numpy=\narray([[-0.041],\n       [-0.441],\n       [-0.36 ],\n       [-0.136],\n       [-0.681],\n       [-0.46 ],\n       [-0.556],\n       [ 0.235],\n       [ 0.158]], dtype=float32)>"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.layers[1].kernel"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [],
   "source": [
    "linear_model.compile(\n",
    "    optimizer=tf.optimizers.Adam(learning_rate=0.1),\n",
    "    loss='mean_absolute_error'\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.4 s, sys: 149 ms, total: 2.55 s\n",
      "Wall time: 2.02 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = linear_model.fit(\n",
    "    train_features, train_labels,\n",
    "    epochs=100,\n",
    "    verbose=0,\n",
    "    validation_split=0.2\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "test_results['linear_model'] = linear_model.evaluate(\n",
    "    test_features, test_labels, verbose=0\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "def build_and_compile_model(norm):\n",
    "    model = keras.Sequential([\n",
    "        norm,\n",
    "        layers.Dense(64, activation='relu'),\n",
    "        layers.Dense(64, activation='relu'),\n",
    "        layers.Dense(1)\n",
    "    ])\n",
    "\n",
    "    model.compile(loss='mean_absolute_error',\n",
    "                  optimizer=tf.keras.optimizers.Adam(0.001))\n",
    "\n",
    "    return  model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "normalization_1 (Normalizati (None, 1)                 3         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                128       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,356\n",
      "Trainable params: 4,353\n",
      "Non-trainable params: 3\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "dnn_horsepower_model = build_and_compile_model(horsepower_normalizer)\n",
    "dnn_horsepower_model.summary()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.63 s, sys: 168 ms, total: 2.79 s\n",
      "Wall time: 2.35 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = dnn_horsepower_model.fit(\n",
    "    train_features['Horsepower'], train_labels,\n",
    "    validation_split=0.2,\n",
    "    verbose=0, epochs=100\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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/2jm3yzm3ASg6pFPPXrVArXPuGX/+fuDth7mtQDAzdoXHUtyuYRtEZOD6SvwV3Wecc3/fbXYsh8E5txXYaGbT/UVn4zX7SC/qo9WUdm6HnB7iEpGB6SvxP2Nml++/0Mw+Czw7gP1eDdzjPydwIvCDAWwrEJoTNYTJQfO2fIciIiNcX905vwg8ZGafBFb4y+YAceDDh7tT59xKvLZ+6af21HhoBBpq9wzVLCJyOPrqzrkdeIeZnQUc5y/+nXPuiSGPTPaRKZ7ofWjYCJNPyW8wIjKi9TVkQwL4HHAUsAq4wzmXGY7AZF/OH7bBNWzC8hyLiIxsfbXx34XXJLMKeD/wL0MekfSoqLSCRpcks1tP74rIwPTVxj/TOTcbwMzuYGA3dGUAKlJRNrsxHFm3gWi+gxGREa2vGn9n1wc18eRXeSrGZlcFDXrpuogMTF81/hPMrNH/bEDSnzfAOedKhzQ62aMiFeNVV0Wk+a/5DkVERri+evWEhysQ6V1FKsomN4Zox26o3wDlR+Q7JBEZoXpt6jGzyt6m4QpSoCwV5eHcO+gMJ+Hha6CXwfVERHrTV1PPTryxdbra97v3JHTAtKEISg5UnoxR68by9JRrmf/6j+D5O2HuZfkOS0RGoL5u7v4MqAMeAS4BpjnnpvqTkv4wikVCFMcjLC37AEw9E/7wTa/JR0TkEPWa+J1zX8AbS+fXwKeBv5rZjWY2dehDk/2Vp6LUt2XhvP/wFvzmKuhsz29QIjLi9FXjp2soaOCrwK3AZcB7hjowOVBFKkZdaxoqjoRzfwhvLoX/cyqsfSTfoYnICNLXzd0iM/ukmf0G+D1QDMxxzv3nsEQn+yhPRalr9R+tePvF8OmHIByDexfCry6E9oa8xiciI0NfNf7teDX9ZcC/Am8Ac83sAjO7YKiDk31VpGLUt3Z7PfHb5sPn/gTv/R689hgsvgSynQffgIgIfffq+TVe753p/tSdAx4ciqCkZxWpKHUt+72XPhKDd14DyXJ4+Gr4/XXwgZ/kJT4RGRn6eoDr0mGKQ/qhPBWjsT1DNucIh/Ybo/PtF8Ou1+FP/wZjjmbvKNoiIvvqq43/A31toD/ryOCoLk0AsLm+recVzv4OHPtBePQbjN3+p2GMTERGkr6aem4ys03Q6xDwPwB+O3ghycEcO74EgDVbGplcmTpwhVAIzr8Nms/n2DU/htdOhaPUAUtE9tVX4t8G/LiPdV4dpFikD9NrSjCDl7c08r7janpeKZaCT95Hy83zKFn0Kbj4ITjitGGNU0QKW19t/POGKQ7ph1QswtQxRazZ0tj7islyXjz+u7xz7ffgno/DeT+FmR8G07u7RKQfD3BJYTl2fCkv95X4gc5YudfPv2wi/PpS+M/58PoSDe4mIkr8I83M8aVs3N1GY3s/+uuXT4bPPQ0fvgVadsIvPww/PQF+92Xvad+OpiGPV0QKT19t/JhZCDjNOffnYYhH+jBzgvfum1e2NHHK1H6MjB0Kw4mfhFkfgRfu9RL+yl/Bc7eDhWHCSTDlXTD1DDjidIgVDXEJRCTf+kz8zrmcmd0MnDQM8UgfZo73Ev/Lmxv6l/i7ROIw51JvynTAhmXw5h/hradh2X94/f9DUZg0F6YvgBMvgqKqoSiCiORZn4nf97iZfQR40Dk1EufTuJI4VUUx1mwZQDNNJA7T5nkTQLoFNvzFG/TtjSfhsW/BE9+DY8+DU66AI04dhMhFpFD0N/F/FvgSkDWzNvTO3bwxs37f4O23WBEcdbY3AWx72XvRywuL4KX7YfJp8K4vwNHneM8KiMiI1q//i51zJc65kHMu6pwr9eeV9PNk5oRS1m5rIpPNDc0OqmfCghvhy2vg/TdC42a490L42Ynw6De8q4PcEO1bRIZcf2v8mNl5wJn+7JPOOT2tmyczx5eSzuR4Y2cLx1SXDN2OYkVw6mdh7mfg5Ye8K4Bn/q93T6C4Bo7/GBx/IdTMGroYRGTQ9Svxm9mPgJOBe/xF15rZO51zXxuyyOSgjt1zg7dxaBN/l3AEZn/Um9ob4NXH4KUH4C+3wJ//HcbO8IaGeNtZcOQ7IJoc+phE5LD1t8a/ADjROZcDMLO7gL8CSvx5MG1sEbFIiDVbGvnwSROHd+eJsr0ngZZdsPpBWPPf8Oxt3pVAOO7dDJ56JkydBxPn6L6ASIHpd1MPUA7s9j+XDX4o0l/RcIhjqosH9wbv4SiqglMu96Z0K6z/k/d08FtL4YkbgBugaBzMWAAzPug9LxBN5DdmEel34v8B3ovWl+D16DkTuH7IopI+zRxfyuNrtuOcwwphDJ5YCo5+rzeBdzXwxhJ45bew6n6vl1Ak6SX/o86GmuNh3LGQOoRnEURkUPT3yd0ccBpeOz/APzjntg5lYNK7meNLWby8lmff3M2p0wrwQauiqr1NQp3t3oNirz3m3R945LFu642DiW+HSSfD5FO8E0KyPG9hiwRBf5/c/apzbjHw8DDEJP3woRMncvey9Vx+93IWf+50ZtQUcO/aaAKOfo83vf+foWETbF8DO16Bbath03JY98je9csmw7iZMG4GjJkOY6dDxRRIVWmEUZFB0N+mnv81s68A9wEtXQudc7sP/hMZShVFMe7+zCl89JZlXHzHs9z/uXdwRFUPL2cpRGUTvenobi+Jad0Nm56HbS95J4Ntq+H1JyDXbTC6cBxKJ0DlVP+EcIx3gqg+DuLD0LtJZJTob+Jf6P+9qtsyB0w73B2bWRhYDmxyzun1jYdhUkWKuz9zCh+7dRmfuuMZvvb+GbxnZjXR8AjsRZOq3PceAUA2A3Vvwc61UL8RGjdBQy3sfh1W3AWdrf6KBpXTmE0Z7LgT4qVQPA4q3wZVb/P+pip1tSDi628b//XOufsGed/XAmuAAm6jKHzHVJdw52Unc9U9K7jynhWMLYnz8bmTmBkaBUMqhSMw5ihv2l8u550Itq2Gratg6wtEa1+BrS9BR6M3DLXL7l0/VuI1F5UfASXVUFwNJTXefPmRXvNSJDZsRRPJp/628V+H18wzKMxsEvA3wPfxxgCSATjpiAqWfnU+T63bwb3PbuCWJ1+nJGaEx2/l3FkHeUXjSBcKee8bKJ8M088FYMWTTzJv3jzv+0wa6tfDrte9K4S69d7VQ91bsPEv0LrrwG3GSyFRDqkKKBnvTaUTvKuHonHeyaJ4nDdF4sNUUJHBZ/0ZbNN/cncng9TGb2b3Az8ESoCv9NTUY2ZXAFcAVFdXz1m0aNHh7Irm5maKi4sP67cj1VsNWW5/sY3aFmNudZgFU6NMKQsRGuVNHYdyrC3XSSxdR6J9O8m2bcQ7dhDtbCaSaSLa2US8Yzex9C5inT0/K9EZKSIdqyAdq6QjXkkmUkImUkQmkiIdK6cjPsafKnGh6GAW8wBB/G9cZe6f+fPnP++cm7v/8mFv4zezDwDbnXPPm9m8g63nnLsNuA1g7ty5bk9N7hA92b0WGCCTSpaw1ibz08dfZfm2dqpL47x3ZjXnHFfDadOqRuZ9gD4MybHubIeWHdCyHZr3TtHmbUSbt1HUtBWa3oC2Okj3NFS2eVcIpRO9v4ly7+nnSMy7Ksl2eO9BKJu0d+q6woj373/yIP43rjIPTL8Sv3Nu6qDszfNO4DwzWwAkgFIz+y/n3KcGcR+BFwkZV807iotOPYIla7fzh9XbeOD5TfzXXzZQlozynmOrWTC7hncdPYZ4JJzvcAtXNLG3Sakvuax3f6F5u3cTunGT13W1sdu0/WVoa4Bs2kv+kYR3culoOHB74RiEIt6b0iIxSI3xTwpj/KansZCqombL67Big/c+5Wwasp1eb6iSCd7N7aq3efc4NHSG+HpN/H7//Rv9zx9zzv2623c/cM59/VB36A/s9jV/G/PwmnqU9IdIeSrG+SdN4vyTJtHemWXpuh08snorj728lQdW1FIcj3D2seOYN30sp06tYkK5Blg7bKEwJCu8aez0Q/tte6N/sti89+qibbd3MnE5761pLTu8acuL3s1r/2QxA2BtP/djIYgVe1ceyTLvhBBNek9eRxJel9lwdO9JJxT27mdEU97vognvCqVrnUSZN8VLvHUt7PWeymW82HMZ74S0TwwGmHdy6jpRZdN7P3dfP5byt1/qbT/TAZkOiprf8u7fROLetrId3hXUnhv6tvffLdMOmTboaIZ0s7ePskneTf3SCd52Xc6Lt73e61rc3uCVN1nu/VtFE3v/TVp3eceqaYs3n6z07gs55135tdV75Y7E/SnhjXQbTXr/Pp1tXo80l/X2ESvytt3Z5sWaTXvrRou88keLBv2k3VeN/0LgRv/z14Bfd/vuXOCQE7/kTyIa5n3H1fC+42pIZ3L8+fWd/M+qrfzh5a38ZuVmACZXJplzRAXHTyrn+EllHDehjGRMVwRDLlEKiZneuxD6q7Md2naz7M9/4vTTT/eWhWN+ggp7Vxu7XvNubne2+ckt471xra3eS3LpFu9v4+a9ybMrCXcl7kw7Xstu4TgZvM7gQfD5Z7yHGQdRX4nfDvK5p/lD5px7EnhyoNuRQxeLhJg3fRzzpo/jB7nZrNnSyLNv7uaZN3ex7I1dPOSfCMIhY3p1CScd4Z0Ijh1fyjHVJSSiOhnkXTQB0Ql0JMZ6Ndj9jZsxOAnDOS/5p1u8mmou4z1jkWn3mrba6r2adNfVict5NeFw1LvCsO61Vbe3Rh+K+Ceq6N4TVjjW7XkL552w2uq9GrjLebXnSIyXVq9m1vSjvBhczrtSicS8GnUXM3/9uDdOVLzYq2GHwl6NvX6Dd8LDAeYtT5R5NfhEmVfWrhp8pt2/Qun0vi+b6F0t5HLeFUDrrr1XfIlyrytyJu1fbXT927V5tfxoypvMvMEN0817a/mRpPfbrquCdKvX/XiQ9ZX43UE+9zQvI1Q4ZMyaWMasiWX87bu82znbGtt5sbaBFzbWs3JjPQ+v3Mw9z2zYs/6UqhRHjSveM02vLuVt44p0v2A0MvObHgqnGXDn9jI4ft7hb6BsEhxx2qDFM9L0lfhPMLNGvNp90v+MP6/xdUex6tIE752Z4L0zvdpGLudYv7uVNVsaWbOlkXXbmnhtezOPr9lOJufVASIhY8qYIqZUFTGlKsURVSnGlcQZWxJnXEmCieVJQqHR3aVUZCToNfE751R9EwBCIWPqmCKmjiliwezxe5Z3ZnO8ubOFV7Y2sXZrI+u2NbNhVytPv7aD9s5938ubioU5prqEY6qLqSlNUFXsnRQmlCeZVJGkqihWGENMi4xyh/IiFpEDeC+FKfFeAXnChD3LnXPsbE6zvamdHU0dbGloZ+3WJtZubeKJV3awu6WD3H6NhYloiEkVKSZVJJlYnqS6NEFVcYyqojhjS2KMLU4wpiRGKqb/bEUGQv8HyZAwM8b6zTw9yeYcda1ptjd2sLm+jdq6VjbWtbGpro3a+lZWbqynvrWzx9+mYmHGFMcZUxyjsihOZVGUilSMrZvS/LVzHQAliQjTa0qYUVN60BhEgkqJX/IiHDI/eceZOaHncfrSmRx1rWl2NHWws7mDnc17P3f9ra1rZdWmNHWtnaQzOXj91QO2U5KIMK7E29eYkjhji+NUFsWoKIpREo+QioUpikeIR0LEI2GSsRDVpQlKEkM71IJIvijxS8GKRbwEXF3adz8C5xxPPfXUnkfadzV3sHZrE69sbWL9rpY9J401mxv5Y3MHje2ZPrdZkogwoSxJccI7OSSjYcaUxKkuSTCuNE48srebYjhkhENGJGRUpGJMKE9SU5YYlUNjyMinxC+jwv43hauK47zjqDjvOGpMj+unMznqW9O0pLO0dGRo6ciQzubo6MzRks6wtaGdzfVtbGlopyWdobkjw7bGdpavr2N3S7qfMUF5MkppMkpZMkokZKSzOTozDjMojkcoikdIREN7urbHIiHGlSSoKYtTnorhnCOTczgHRfEwRbEIxfEIyViYVMz77Y7WHLV1rWSyjle2NrFqUz1rtzZzZFWK06ZVccqUSspSunqRvZT4JZBikRDj+nEl0ZOOTJadzWkyWa/XknOQc45szpHO5tjdkmZzfRub6tvZ3dJBY1uGhrZOMrkcZeEosUiIbA5aOjLUtabp6MzteWapI5PjicbttKazvUTQg6VL9nwMh4wjq1IsfXUHdzz9JgBjiuOMK4kzrjROstvDd6GQEQuHiIVDRMJGNBwiGjZCZuScI+e8G/gVqSgVRTGKYhGyzpHz78wnomESUe/36WyOdCZHJuf2/BagKBbec5JzDrLOkcnm2N7UsefkGg4ZRbEIRfEw5akYVX5TXCwcwuGd+CJhIx4JE4+E2N2eY+PuVrI5t2d/2ZwjHDKK4xFKEhGS0TDhkA2op5jzy9HWmaW1w6sAbGloZ+PuVmrr2qgqjnHC5HJmji8dUQ81KvGLHKJ4JMzEIRzTyDlHc0eG+tbOPU1I4J0oWtNZmjsytKWztHVmaUtneXnNGqbPmIEBR40r5lg/CbV3ZnlhYz3L19dRW9fG9sZ2tjW1e/dCfJmco7MrYWe9z51ZL5GGQ94JoCOTpTM7dM9rdl3xdGRyfa/c3ZNL+l4H78orZEbXEPT7lyRkRtgMM++7rmSfc+6AYYb2/R17Tm7RsFGWjBHy9+Wd+LyTkgGRbifXsO09ph2ZvSfLSMiIhL0TsXdFFyYZi/CD82cxqWJwX6uqxC9SYMyMkkS03zeXq5peY97cA0cPTUTDnDqtilOnVQ0oHuccreksu1vStKazhEMQDoVwztHemaOtM0tnNkc0HCIe2ZvcvJq2o6XDO1k1d2QwvCuSSDjE2OI4E8uTlCYjmBmZbI6Wjiz1bWl2taSpa0nTmc0BXlLO5RwdmRztnVnWrl3LzGNnEPGvTsIhb5+ZnKOlI0NTu3eS7ErAOecwfzuwd7wZB3uuTnI5h5kRMu9k0VWGkBnJWIiUf0VSXZrgiMoU48uS7GjqYOXGel6o7eqF5l157IkpZN6IFznvhNqZzZH1r1CcY0+HglDIyOa8k29HNkd7OktLOkNDa3pInm1R4heRXpkZRX5TzVCKhEOUpUKUpaIcWVXU67pPtr7R48luuNWUJTi3rGbEvelOXQ5ERAJGiV9EJGCU+EVEAkaJX0QkYJT4RUQCRolfRCRglPhFRAJGiV9EJGCU+EVEAkaJX0QkYJT4RUQCRolfRCRglPhFRAJGiV9EJGCU+EVEAkaJX0QkYJT4RUQCRolfRCRglPhFRAJGiV9EJGCU+EVEAkaJX0QkYIY98ZvZZDNbYmYvm9lqM7t2uGMQEQmySB72mQG+7JxbYWYlwPNm9phz7uU8xCIiEjjDXuN3zm1xzq3wPzcBa4CJwx2HiEhQmXMufzs3mwIsBWY55xr3++4K4AqA6urqOYsWLTqsfTQ3N1NcXDzASEeeIJY7iGWGYJZbZe6f+fPnP++cm3vAF865vExAMfA8cEFf686ZM8cdriVLlhz2b0eyIJY7iGV2LpjlVpn7B1juesipeenVY2ZR4AHgHufcg/mIQUQkqPLRq8eAO4A1zrkfD/f+RUSCLh81/ncCnwbOMrOV/rQgD3GIiATSsHfndM49Ddhw71dERDx6cldEJGCU+EVEAkaJX0QkYJT4RUQCRolfRCRglPhFRAJGiV9EJGCU+EVEAkaJX0QkYJT4RUQCRolfRCRglPhFRAJGiV9EJGCU+EVEAkaJX0QkYJT4RUQCRolfRCRglPhFRAJGiV9EJGCU+EVEAkaJX0QkYJT4RUQCRolfRCRglPhFRAJGiV9EJGCU+EVEAkaJX0QkYJT4RUQCRolfRCRglPhFRAJGiV9EJGCU+EVEAkaJX0QkYJT4RUQCRolfRCRg8pL4zexcM1trZq+Z2fX5iEFEJKiGPfGbWRi4GXg/MBP4hJnNHO44RESCKh81/lOA15xzbzjn0sAi4EN5iENEJJAiedjnRGBjt/la4NT9VzKzK4Ar/NlmM1t7mPsbA+w8zN+OZEEsdxDLDMEst8rcP0f2tDAfib9fnHO3AbcNdDtmttw5N3cQQhpRgljuIJYZgllulXlg8tHUswmY3G1+kr9MRESGQT4S/3PA0WY21cxiwIXAw3mIQ0QkkIa9qcc5lzGzvwceBcLAz51zq4dwlwNuLhqhgljuIJYZgllulXkAzDk3WNsSEZERQE/uiogEjBK/iEjAjOrEH4ShIcxsspktMbOXzWy1mV3rL680s8fM7FX/b0W+Yx1sZhY2s7+a2W/9+alm9ox/vO/zOw+MKmZWbmb3m9krZrbGzE4f7cfazL7o/7f9kpnda2aJ0XiszeznZrbdzF7qtqzHY2uen/nlf9HM3n4o+xq1iT9AQ0NkgC8752YCpwFX+eW8HnjcOXc08Lg/P9pcC6zpNv/PwE+cc0cBdcBn8hLV0Pop8IhzbgZwAl75R+2xNrOJwDXAXOfcLLwOIRcyOo/1ncC5+y072LF9P3C0P10B3HIoOxq1iZ+ADA3hnNvinFvhf27CSwQT8cp6l7/aXcCH8xLgEDGzScDfALf78wacBdzvrzIay1wGnAncAeCcSzvn6hnlxxqv92HSzCJACtjCKDzWzrmlwO79Fh/s2H4IuNt5/gKUm9n4/u5rNCf+noaGmJinWIaFmU0BTgKeAaqdc1v8r7YC1fmKa4j8G/BVIOfPVwH1zrmMPz8aj/dUYAfwC7+J63YzK2IUH2vn3CbgX4ANeAm/AXie0X+suxzs2A4ov43mxB8oZlYMPAB8wTnX2P075/XZHTX9ds3sA8B259zz+Y5lmEWAtwO3OOdOAlrYr1lnFB7rCrza7VRgAlDEgc0hgTCYx3Y0J/7ADA1hZlG8pH+Pc+5Bf/G2rks//+/2fMU3BN4JnGdmb+E14Z2F1/Zd7jcHwOg83rVArXPuGX/+frwTwWg+1u8B3nTO7XDOdQIP4h3/0X6suxzs2A4ov43mxB+IoSH8tu07gDXOuR93++ph4BL/8yXAb4Y7tqHinPuac26Sc24K3nF9wjl3EbAE+Ki/2qgqM4Bzbiuw0cym+4vOBl5mFB9rvCae08ws5f+33lXmUX2suznYsX0YuNjv3XMa0NCtSahvzrlROwELgHXA68A38h3PEJXxXXiXfy8CK/1pAV6b9+PAq8D/ApX5jnWIyj8P+K3/eRrwLPAa8Gsgnu/4hqC8JwLL/eP9EFAx2o818I/AK8BLwC+B+Gg81sC9ePcxOvGu7j5zsGMLGF6vxdeBVXi9nvq9Lw3ZICISMKO5qUdERHqgxC8iEjBK/CIiAaPELyISMEr8IiIBo8QvAphZ1sxWdpsGbaAzM5vSfcRFkXwb9lcvihSoNufcifkOQmQ4qMYv0gsze8vMbjSzVWb2rJkd5S+fYmZP+GOhP25mR/jLq83s/5nZC/70Dn9TYTP7T39c+T+YWTJvhZLAU+IX8ST3a+pZ2O27BufcbOA/8EYFBfh34C7n3PHAPcDP/OU/A55yzp2AN47Oan/50cDNzrnjgHrgI0NaGpFe6MldEcDMmp1zxT0sfws4yzn3hj8Y3lbnXJWZ7QTGO+c6/eVbnHNjzGwHMMk519FtG1OAx5z3Mg3M7B+AqHPuhmEomsgBVOMX6Zs7yOdD0dHtcxbdX5M8UuIX6dvCbn+X+Z//jDcyKMBFwB/9z48DV8KedwKXDVeQIv2lWoeIJ2lmK7vNP+Kc6+rSWWFmL+LV2j/hL7sa701Y1+G9Fesyf/m1wG1m9hm8mv2VeCMuihQMtfGL9MJv45/rnNuZ71hEBouaekREAkY1fhGRgFGNX0QkYJT4RUQCRolfRCRglPhFRAJGiV9EJGD+PwcWk9tKoku/AAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [],
   "source": [
    "x = tf.linspace(0.0, 250, 251)\n",
    "y = dnn_horsepower_model.predict(x)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_horsepower(x, y)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "test_results['dnn_horsepower_model'] = dnn_horsepower_model.evaluate(\n",
    "    test_features['Horsepower'], test_labels,\n",
    "    verbose=0\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "normalization (Normalization (None, 9)                 19        \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 64)                640       \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,884\n",
      "Trainable params: 4,865\n",
      "Non-trainable params: 19\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "dnn_model = build_and_compile_model(normalizer)\n",
    "dnn_model.summary()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.49 s, sys: 155 ms, total: 2.65 s\n",
      "Wall time: 1.99 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = dnn_model.fit(\n",
    "    train_features, train_labels,\n",
    "    validation_split=0.2,\n",
    "    verbose=0, epochs=100\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [],
   "source": [
    "test_results['dnn_model'] = dnn_model.evaluate(test_features, test_labels, verbose=0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "                      Mean absolute error [MPG]\nhorsepower_model                       3.631874\nlinear_model                           2.533477\ndnn_horsepower_model                   2.943143\ndnn_model                              1.689879",
      "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>Mean absolute error [MPG]</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>horsepower_model</th>\n      <td>3.631874</td>\n    </tr>\n    <tr>\n      <th>linear_model</th>\n      <td>2.533477</td>\n    </tr>\n    <tr>\n      <th>dnn_horsepower_model</th>\n      <td>2.943143</td>\n    </tr>\n    <tr>\n      <th>dnn_model</th>\n      <td>1.689879</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = dnn_model.predict(test_features).flatten()\n",
    "\n",
    "a = plt.axes(aspect='equal')\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [MPG]')\n",
    "plt.ylabel('Predictions [MPG]')\n",
    "lims = [0, 50]\n",
    "plt.xlim(lims)\n",
    "plt.ylim(lims)\n",
    "_ = plt.plot(lims, lims)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = test_predictions - test_labels\n",
    "plt.hist(error, bins=25)\n",
    "plt.xlabel('Prediction Error [MPG]')\n",
    "_ = plt.ylabel('Count')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "data": {
      "text/plain": "array([16.29 , 10.832, 10.825, 25.153, 20.013, 12.884, 13.175, 12.528,\n       19.29 , 31.029, 24.281, 26.844, 14.888, 25.517, 12.936, 14.716,\n       15.035, 13.285, 18.286, 12.734, 14.034, 25.438, 18.432, 20.281,\n       30.112, 23.625, 16.644, 23.183, 17.279, 18.973, 27.988, 21.328,\n       18.58 , 20.096, 26.514, 16.258, 18.872, 30.537, 29.371, 17.74 ,\n       30.62 , 28.8  , 15.864, 36.231, 34.634, 35.923, 20.319, 21.601,\n       20.123, 25.251, 30.841, 17.524, 31.754, 17.843, 17.392, 17.746,\n       32.473, 34.156, 22.069, 25.275, 39.058, 32.743, 27.589, 26.997,\n       32.111, 39.387, 36.477, 35.801, 33.159, 29.949, 21.947, 21.79 ,\n       29.966, 30.997, 36.349, 35.718, 37.99 , 30.243], dtype=float32)"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "data": {
      "text/plain": "9      15.0\n25     10.0\n28      9.0\n31     25.0\n33     19.0\n       ... \n369    34.0\n375    36.0\n382    34.0\n384    32.0\n396    28.0\nName: MPG, Length: 78, dtype: float64"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}