zadanie3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Marek Ochocki (marcopolo97@vp.pl) i Łukasz Gosek (lukaszjgosek@gmail.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zadanie 3\n",
    "\n",
    "Reading data from DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>164.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>4</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>164.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>5</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>158.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2844</td>\n",
       "      <td>ohc</td>\n",
       "      <td>5</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.5</td>\n",
       "      <td>110.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>17710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>158.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.9</td>\n",
       "      <td>3086</td>\n",
       "      <td>ohc</td>\n",
       "      <td>5</td>\n",
       "      <td>131</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.3</td>\n",
       "      <td>140.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>23875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>192.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>2.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2395</td>\n",
       "      <td>ohc</td>\n",
       "      <td>4</td>\n",
       "      <td>108</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>8.8</td>\n",
       "      <td>101.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>16430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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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",
       "      <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>154</th>\n",
       "      <td>-1</td>\n",
       "      <td>95.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2952</td>\n",
       "      <td>ohc</td>\n",
       "      <td>4</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>-1</td>\n",
       "      <td>95.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.8</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3049</td>\n",
       "      <td>ohc</td>\n",
       "      <td>4</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>-1</td>\n",
       "      <td>95.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3012</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>6</td>\n",
       "      <td>173</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>-1</td>\n",
       "      <td>95.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3217</td>\n",
       "      <td>ohc</td>\n",
       "      <td>6</td>\n",
       "      <td>145</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>-1</td>\n",
       "      <td>95.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3062</td>\n",
       "      <td>ohc</td>\n",
       "      <td>4</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>159 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  normalized-losses   make fuel-type aspiration  num-of-doors  \\\n",
       "0            2              164.0   audi       gas        std           4.0   \n",
       "1            2              164.0   audi       gas        std           4.0   \n",
       "2            1              158.0   audi       gas        std           4.0   \n",
       "3            1              158.0   audi       gas      turbo           4.0   \n",
       "4            2              192.0    bmw       gas        std           2.0   \n",
       "..         ...                ...    ...       ...        ...           ...   \n",
       "154         -1               95.0  volvo       gas        std           4.0   \n",
       "155         -1               95.0  volvo       gas      turbo           4.0   \n",
       "156         -1               95.0  volvo       gas        std           4.0   \n",
       "157         -1               95.0  volvo    diesel      turbo           4.0   \n",
       "158         -1               95.0  volvo       gas      turbo           4.0   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  length  width  \\\n",
       "0        sedan          fwd           front        99.8   176.6   66.2   \n",
       "1        sedan          4wd           front        99.4   176.6   66.4   \n",
       "2        sedan          fwd           front       105.8   192.7   71.4   \n",
       "3        sedan          fwd           front       105.8   192.7   71.4   \n",
       "4        sedan          rwd           front       101.2   176.8   64.8   \n",
       "..         ...          ...             ...         ...     ...    ...   \n",
       "154      sedan          rwd           front       109.1   188.8   68.9   \n",
       "155      sedan          rwd           front       109.1   188.8   68.8   \n",
       "156      sedan          rwd           front       109.1   188.8   68.9   \n",
       "157      sedan          rwd           front       109.1   188.8   68.9   \n",
       "158      sedan          rwd           front       109.1   188.8   68.9   \n",
       "\n",
       "     height  curb-weight engine-type  num-of-cylinders  engine-size  \\\n",
       "0      54.3         2337         ohc                 4          109   \n",
       "1      54.3         2824         ohc                 5          136   \n",
       "2      55.7         2844         ohc                 5          136   \n",
       "3      55.9         3086         ohc                 5          131   \n",
       "4      54.3         2395         ohc                 4          108   \n",
       "..      ...          ...         ...               ...          ...   \n",
       "154    55.5         2952         ohc                 4          141   \n",
       "155    55.5         3049         ohc                 4          141   \n",
       "156    55.5         3012        ohcv                 6          173   \n",
       "157    55.5         3217         ohc                 6          145   \n",
       "158    55.5         3062         ohc                 4          141   \n",
       "\n",
       "    fuel-system  bore  stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "0          mpfi  3.19    3.40               10.0       102.0    5500.0   \n",
       "1          mpfi  3.19    3.40                8.0       115.0    5500.0   \n",
       "2          mpfi  3.19    3.40                8.5       110.0    5500.0   \n",
       "3          mpfi  3.13    3.40                8.3       140.0    5500.0   \n",
       "4          mpfi  3.50    2.80                8.8       101.0    5800.0   \n",
       "..          ...   ...     ...                ...         ...       ...   \n",
       "154        mpfi  3.78    3.15                9.5       114.0    5400.0   \n",
       "155        mpfi  3.78    3.15                8.7       160.0    5300.0   \n",
       "156        mpfi  3.58    2.87                8.8       134.0    5500.0   \n",
       "157         idi  3.01    3.40               23.0       106.0    4800.0   \n",
       "158        mpfi  3.78    3.15                9.5       114.0    5400.0   \n",
       "\n",
       "     city-mpg  highway-mpg    price  \n",
       "0          24           30  13950.0  \n",
       "1          18           22  17450.0  \n",
       "2          19           25  17710.0  \n",
       "3          17           20  23875.0  \n",
       "4          23           29  16430.0  \n",
       "..        ...          ...      ...  \n",
       "154        23           28  16845.0  \n",
       "155        19           25  19045.0  \n",
       "156        18           23  21485.0  \n",
       "157        26           27  22470.0  \n",
       "158        19           25  22625.0  \n",
       "\n",
       "[159 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.max_columns', 500)\n",
    "\n",
    "columns = ['symboling',  'normalized-losses',  'make',  'fuel-type',  'aspiration',  'num-of-doors',  'body-style',  'drive-wheels',  'engine-location',  'wheel-base',  'length',  'width',  'height',  'curb-weight',  'engine-type',  'num-of-cylinders',  'engine-size',  'fuel-system',  'bore',  'stroke',  'compression-ratio',  'horsepower',  'peak-rpm',  'city-mpg',  'highway-mpg',  'price']\n",
    "\n",
    "# function converting words denoting numbers into actual numbers\n",
    "text_2_num_conv = lambda x: 8 if x=='eight' else (5 if x=='five' else (4 if x=='four' else (6 if x=='six' else (3 if x=='three' else (12 if x=='twelve' else (2 if x=='two' else np.nan))))))\n",
    "\n",
    "data = pd.read_csv(\"imports-85.data\", header=None, names=columns, converters={'num-of-doors': text_2_num_conv, 'num-of-cylinders': text_2_num_conv}, na_values='?')\n",
    "\n",
    "# removing rows with missing values\n",
    "data.dropna(inplace=True)\n",
    "\n",
    "data.reset_index(inplace=True, drop=True)\n",
    "\n",
    "display(data)\n",
    "\n",
    "# separating target from features\n",
    "target = data['price']\n",
    "features = data.drop(labels=['price'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One Hot encoding categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "columns_for_one_hot = ['make',  'fuel-type',  'aspiration', 'body-style',  'drive-wheels',  'engine-location',  'engine-type', 'fuel-system']\n",
    "\n",
    "one_hot_encoder = OneHotEncoder(categories=[\n",
    "    ['alfa-romero', 'audi', 'bmw', 'chevrolet', 'dodge', 'honda', 'isuzu', 'jaguar', 'mazda', 'mercedes-benz', 'mercury', 'mitsubishi', 'nissan', 'peugot', 'plymouth', 'porsche', 'renault', 'saab', 'subaru', 'toyota', 'volkswagen', 'volvo'],\n",
    "    ['diesel', 'gas'],\n",
    "    ['std', 'turbo'],\n",
    "    ['hardtop', 'wagon', 'sedan', 'hatchback', 'convertible'],\n",
    "    ['4wd', 'fwd', 'rwd'],\n",
    "    ['front', 'rear'],\n",
    "    ['dohc', 'dohcv', 'l', 'ohc', 'ohcf', 'ohcv', 'rotor'],\n",
    "    ['1bbl', '2bbl', '4bbl', 'idi', 'mfi', 'mpfi', 'spdi', 'spfi']\n",
    "], sparse=False)\n",
    "\n",
    "one_hot_encoder.fit(features[columns_for_one_hot])\n",
    "one_hot_encoded = pd.DataFrame(one_hot_encoder.transform(features[columns_for_one_hot]))\n",
    "one_hot_encoded\n",
    "\n",
    "X = pd.concat([one_hot_encoded, features.drop(labels=columns_for_one_hot, axis=1)], axis=1).astype(float)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising our feature matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2952.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.8</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3049.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3012.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3217.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>3062.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>159 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4    5    6    7    8    9   10   11   12   13  \\\n",
       "0    0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1    0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2    0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3    0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "154  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "155  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "156  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "157  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "158  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "      14   15   16   17   18   19   20   21   22   23   24   25   26   27  \\\n",
       "0    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0   \n",
       "1    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0   \n",
       "2    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0   \n",
       "3    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0   \n",
       "4    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "154  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  1.0  0.0  0.0  0.0   \n",
       "155  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  1.0  0.0  0.0   \n",
       "156  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  1.0  0.0  0.0  0.0   \n",
       "157  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  1.0  0.0  0.0   \n",
       "158  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  1.0  0.0  0.0   \n",
       "\n",
       "      28   29   30   31   32   33   34   35   36   37   38   39   40   41  \\\n",
       "0    1.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "1    1.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "2    1.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "3    1.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "4    1.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "154  1.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "155  1.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "156  1.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0   \n",
       "157  1.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "158  1.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       "\n",
       "      42   43   44   45   46   47   48   49   50  symboling  \\\n",
       "0    0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0        2.0   \n",
       "1    0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0        2.0   \n",
       "2    0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0        1.0   \n",
       "3    0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0        1.0   \n",
       "4    0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0        2.0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...        ...   \n",
       "154  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0       -1.0   \n",
       "155  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0       -1.0   \n",
       "156  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0       -1.0   \n",
       "157  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0       -1.0   \n",
       "158  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0       -1.0   \n",
       "\n",
       "     normalized-losses  num-of-doors  wheel-base  length  width  height  \\\n",
       "0                164.0           4.0        99.8   176.6   66.2    54.3   \n",
       "1                164.0           4.0        99.4   176.6   66.4    54.3   \n",
       "2                158.0           4.0       105.8   192.7   71.4    55.7   \n",
       "3                158.0           4.0       105.8   192.7   71.4    55.9   \n",
       "4                192.0           2.0       101.2   176.8   64.8    54.3   \n",
       "..                 ...           ...         ...     ...    ...     ...   \n",
       "154               95.0           4.0       109.1   188.8   68.9    55.5   \n",
       "155               95.0           4.0       109.1   188.8   68.8    55.5   \n",
       "156               95.0           4.0       109.1   188.8   68.9    55.5   \n",
       "157               95.0           4.0       109.1   188.8   68.9    55.5   \n",
       "158               95.0           4.0       109.1   188.8   68.9    55.5   \n",
       "\n",
       "     curb-weight  num-of-cylinders  engine-size  bore  stroke  \\\n",
       "0         2337.0               4.0        109.0  3.19    3.40   \n",
       "1         2824.0               5.0        136.0  3.19    3.40   \n",
       "2         2844.0               5.0        136.0  3.19    3.40   \n",
       "3         3086.0               5.0        131.0  3.13    3.40   \n",
       "4         2395.0               4.0        108.0  3.50    2.80   \n",
       "..           ...               ...          ...   ...     ...   \n",
       "154       2952.0               4.0        141.0  3.78    3.15   \n",
       "155       3049.0               4.0        141.0  3.78    3.15   \n",
       "156       3012.0               6.0        173.0  3.58    2.87   \n",
       "157       3217.0               6.0        145.0  3.01    3.40   \n",
       "158       3062.0               4.0        141.0  3.78    3.15   \n",
       "\n",
       "     compression-ratio  horsepower  peak-rpm  city-mpg  highway-mpg  \n",
       "0                 10.0       102.0    5500.0      24.0         30.0  \n",
       "1                  8.0       115.0    5500.0      18.0         22.0  \n",
       "2                  8.5       110.0    5500.0      19.0         25.0  \n",
       "3                  8.3       140.0    5500.0      17.0         20.0  \n",
       "4                  8.8       101.0    5800.0      23.0         29.0  \n",
       "..                 ...         ...       ...       ...          ...  \n",
       "154                9.5       114.0    5400.0      23.0         28.0  \n",
       "155                8.7       160.0    5300.0      19.0         25.0  \n",
       "156                8.8       134.0    5500.0      18.0         23.0  \n",
       "157               23.0       106.0    4800.0      26.0         27.0  \n",
       "158                9.5       114.0    5400.0      19.0         25.0  \n",
       "\n",
       "[159 rows x 68 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting data into train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data standarization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function creating a model - parameters allow to control number of layers, their size and learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "from keras import regularizers\n",
    "from keras import optimizers\n",
    "\n",
    "def create_model(num_layers=1, layer_size=20, lr=0.005):\n",
    "    network = models.Sequential()\n",
    "    network.add(layers.Dense(layer_size,\n",
    "                             activation='relu',\n",
    "                             input_shape=(68,),\n",
    "                             kernel_regularizer=regularizers.l2(0.001),\n",
    "                             activity_regularizer=regularizers.l1(0.001)))\n",
    "    for i in range(num_layers-1):\n",
    "        network.add(layers.Dense(layer_size,\n",
    "                                 activation='relu',\n",
    "                                 kernel_regularizer=regularizers.l2(0.001),\n",
    "                                 activity_regularizer=regularizers.l1(0.001)))\n",
    "    network.add(layers.Dense(1))\n",
    "\n",
    "    adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "    network.compile(optimizer=adam,\n",
    "                    loss='mean_absolute_error',\n",
    "                    metrics=['accuracy'])\n",
    "    \n",
    "    return network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing grid search over model hyperparameters, and learning parameters (number of epochs, batch size) - 10-fold CV instead of Leave One Out due to extremely long computation time for the latter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    5.4s\n",
      "[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    5.5s\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    7.5s\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    9.3s\n",
      "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:   11.3s\n",
      "[Parallel(n_jobs=-1)]: Done  48 tasks      | elapsed:   12.7s\n",
      "[Parallel(n_jobs=-1)]: Done  61 tasks      | elapsed:   16.0s\n",
      "[Parallel(n_jobs=-1)]: Done  74 tasks      | elapsed:   18.8s\n",
      "[Parallel(n_jobs=-1)]: Done  89 tasks      | elapsed:   23.7s\n",
      "[Parallel(n_jobs=-1)]: Done 104 tasks      | elapsed:   27.5s\n",
      "[Parallel(n_jobs=-1)]: Done 121 tasks      | elapsed:   32.0s\n",
      "[Parallel(n_jobs=-1)]: Done 138 tasks      | elapsed:   37.9s\n",
      "[Parallel(n_jobs=-1)]: Done 157 tasks      | elapsed:   43.7s\n",
      "[Parallel(n_jobs=-1)]: Done 176 tasks      | elapsed:   51.2s\n",
      "[Parallel(n_jobs=-1)]: Done 197 tasks      | elapsed:   58.4s\n",
      "[Parallel(n_jobs=-1)]: Done 218 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 241 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 264 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done 289 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done 314 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done 341 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=-1)]: Done 368 tasks      | elapsed:  2.5min\n",
      "[Parallel(n_jobs=-1)]: Done 397 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=-1)]: Done 426 tasks      | elapsed:  3.3min\n",
      "[Parallel(n_jobs=-1)]: Done 457 tasks      | elapsed:  3.7min\n",
      "[Parallel(n_jobs=-1)]: Done 488 tasks      | elapsed:  4.2min\n",
      "[Parallel(n_jobs=-1)]: Done 521 tasks      | elapsed:  4.7min\n",
      "[Parallel(n_jobs=-1)]: Done 554 tasks      | elapsed:  5.3min\n",
      "[Parallel(n_jobs=-1)]: Done 589 tasks      | elapsed:  5.9min\n",
      "[Parallel(n_jobs=-1)]: Done 624 tasks      | elapsed:  6.7min\n",
      "[Parallel(n_jobs=-1)]: Done 661 tasks      | elapsed:  7.4min\n",
      "[Parallel(n_jobs=-1)]: Done 698 tasks      | elapsed:  8.2min\n",
      "[Parallel(n_jobs=-1)]: Done 737 tasks      | elapsed:  9.1min\n",
      "[Parallel(n_jobs=-1)]: Done 776 tasks      | elapsed: 10.1min\n",
      "[Parallel(n_jobs=-1)]: Done 817 tasks      | elapsed: 11.3min\n",
      "[Parallel(n_jobs=-1)]: Done 858 tasks      | elapsed: 12.6min\n",
      "[Parallel(n_jobs=-1)]: Done 901 tasks      | elapsed: 14.2min\n",
      "[Parallel(n_jobs=-1)]: Done 944 tasks      | elapsed: 15.6min\n",
      "[Parallel(n_jobs=-1)]: Done 989 tasks      | elapsed: 17.4min\n",
      "[Parallel(n_jobs=-1)]: Done 1034 tasks      | elapsed: 19.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed: 21.2min finished\n"
     ]
    }
   ],
   "source": [
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "hyperparam_grid = dict(\n",
    "    num_layers=[1, 2, 3],\n",
    "    layer_size=[40, 70, 100],\n",
    "    lr=[0.001, 0.005, 0.01],\n",
    "    epochs=[100, 200, 300, 500],\n",
    "    batch_size=[20]\n",
    ")\n",
    "\n",
    "model = KerasClassifier(build_fn=create_model, verbose=0)\n",
    "grid = GridSearchCV(estimator=model, param_grid=hyperparam_grid, cv=10, n_jobs=-1, verbose=10, refit=False)\n",
    "grid_result = grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters of the best estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'batch_size': 20,\n",
       " 'epochs': 300,\n",
       " 'layer_size': 70,\n",
       " 'lr': 0.01,\n",
       " 'num_layers': 1}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a model with optimal parameters and training on full training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "127/127 [==============================] - 0s 558us/step - loss: 11628.0893 - accuracy: 0.0000e+00\n",
      "Epoch 2/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 11623.4460 - accuracy: 0.0000e+00\n",
      "Epoch 3/300\n",
      "127/127 [==============================] - 0s 55us/step - loss: 11617.0913 - accuracy: 0.0000e+00\n",
      "Epoch 4/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 11607.2408 - accuracy: 0.0000e+00\n",
      "Epoch 5/300\n",
      "127/127 [==============================] - 0s 55us/step - loss: 11592.5335 - accuracy: 0.0000e+00\n",
      "Epoch 6/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 11571.2166 - accuracy: 0.0000e+00\n",
      "Epoch 7/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 11542.9095 - accuracy: 0.0000e+00\n",
      "Epoch 8/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 11506.5593 - accuracy: 0.0000e+00\n",
      "Epoch 9/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 11459.1176 - accuracy: 0.0000e+00\n",
      "Epoch 10/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 11401.9818 - accuracy: 0.0000e+00\n",
      "Epoch 11/300\n",
      "127/127 [==============================] - 0s 55us/step - loss: 11333.8259 - accuracy: 0.0000e+00\n",
      "Epoch 12/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 11253.6118 - accuracy: 0.0000e+00\n",
      "Epoch 13/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 11162.1341 - accuracy: 0.0000e+00\n",
      "Epoch 14/300\n",
      "127/127 [==============================] - 0s 63us/step - loss: 11055.6266 - accuracy: 0.0000e+00\n",
      "Epoch 15/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 10939.3737 - accuracy: 0.0000e+00\n",
      "Epoch 16/300\n",
      "127/127 [==============================] - 0s 55us/step - loss: 10805.7979 - accuracy: 0.0000e+00\n",
      "Epoch 17/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 10662.6565 - accuracy: 0.0000e+00\n",
      "Epoch 18/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 10505.3819 - accuracy: 0.0000e+00\n",
      "Epoch 19/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 10328.2716 - accuracy: 0.0000e+00\n",
      "Epoch 20/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 10145.8073 - accuracy: 0.0000e+00\n",
      "Epoch 21/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 9941.4751 - accuracy: 0.0000e+00\n",
      "Epoch 22/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 9734.3935 - accuracy: 0.0000e+00\n",
      "Epoch 23/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 9508.6301 - accuracy: 0.0000e+00\n",
      "Epoch 24/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 9279.7375 - accuracy: 0.0000e+00\n",
      "Epoch 25/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 9033.9148 - accuracy: 0.0000e+00\n",
      "Epoch 26/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 8783.9223 - accuracy: 0.0000e+00\n",
      "Epoch 27/300\n",
      "127/127 [==============================] - 0s 55us/step - loss: 8525.6450 - accuracy: 0.0000e+00\n",
      "Epoch 28/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 8280.6849 - accuracy: 0.0000e+00\n",
      "Epoch 29/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 8027.4072 - accuracy: 0.0000e+00\n",
      "Epoch 30/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 7800.7496 - accuracy: 0.0000e+00\n",
      "Epoch 31/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 7573.8354 - accuracy: 0.0000e+00\n",
      "Epoch 32/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 7367.2002 - accuracy: 0.0000e+00\n",
      "Epoch 33/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 7158.5105 - accuracy: 0.0000e+00\n",
      "Epoch 34/300\n",
      "127/127 [==============================] - 0s 55us/step - loss: 6961.7443 - accuracy: 0.0000e+00\n",
      "Epoch 35/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 6771.6610 - accuracy: 0.0000e+00\n",
      "Epoch 36/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 6573.3944 - accuracy: 0.0000e+00\n",
      "Epoch 37/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 6371.7153 - accuracy: 0.0000e+00\n",
      "Epoch 38/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 6168.5763 - accuracy: 0.0000e+00\n",
      "Epoch 39/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 5959.8236 - accuracy: 0.0000e+00\n",
      "Epoch 40/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 5749.7818 - accuracy: 0.0000e+00\n",
      "Epoch 41/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 5532.9513 - accuracy: 0.0000e+00\n",
      "Epoch 42/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 5311.8456 - accuracy: 0.0000e+00\n",
      "Epoch 43/300\n",
      "127/127 [==============================] - 0s 55us/step - loss: 5089.7357 - accuracy: 0.0000e+00\n",
      "Epoch 44/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 4862.0817 - accuracy: 0.0000e+00\n",
      "Epoch 45/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 4654.6664 - accuracy: 0.0000e+00\n",
      "Epoch 46/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 4423.5172 - accuracy: 0.0000e+00\n",
      "Epoch 47/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 4226.2279 - accuracy: 0.0000e+00\n",
      "Epoch 48/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 4019.9082 - accuracy: 0.0000e+00\n",
      "Epoch 49/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 3816.4096 - accuracy: 0.0000e+00\n",
      "Epoch 50/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 3641.6286 - accuracy: 0.0000e+00\n",
      "Epoch 51/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 3483.9607 - accuracy: 0.0000e+00\n",
      "Epoch 52/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 3337.8853 - accuracy: 0.0000e+00\n",
      "Epoch 53/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 3211.0814 - accuracy: 0.0000e+00\n",
      "Epoch 54/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 3083.5397 - accuracy: 0.0000e+00\n",
      "Epoch 55/300\n",
      "127/127 [==============================] - 0s 40us/step - loss: 2963.7802 - accuracy: 0.0000e+00\n",
      "Epoch 56/300\n",
      "127/127 [==============================] - 0s 40us/step - loss: 2868.8439 - accuracy: 0.0000e+00\n",
      "Epoch 57/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 2758.0752 - accuracy: 0.0000e+00\n",
      "Epoch 58/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 2684.8421 - accuracy: 0.0079\n",
      "Epoch 59/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 2625.1273 - accuracy: 0.0000e+00\n",
      "Epoch 60/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 2562.4349 - accuracy: 0.0000e+00\n",
      "Epoch 61/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 2506.3008 - accuracy: 0.0000e+00\n",
      "Epoch 62/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 2451.2184 - accuracy: 0.0000e+00\n",
      "Epoch 63/300\n",
      "127/127 [==============================] - 0s 40us/step - loss: 2396.3696 - accuracy: 0.0000e+00\n",
      "Epoch 64/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 2344.1005 - accuracy: 0.0079\n",
      "Epoch 65/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 2299.9088 - accuracy: 0.0000e+00\n",
      "Epoch 66/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 2257.0463 - accuracy: 0.0000e+00\n",
      "Epoch 67/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 2214.0130 - accuracy: 0.0000e+00\n",
      "Epoch 68/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 2169.9219 - accuracy: 0.0000e+00\n",
      "Epoch 69/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 2132.6233 - accuracy: 0.0000e+00\n",
      "Epoch 70/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 2085.0968 - accuracy: 0.0000e+00\n",
      "Epoch 71/300\n",
      "127/127 [==============================] - 0s 42us/step - loss: 2042.9544 - accuracy: 0.0000e+00\n",
      "Epoch 72/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 2000.6077 - accuracy: 0.0000e+00\n",
      "Epoch 73/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1960.0362 - accuracy: 0.0000e+00\n",
      "Epoch 74/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1926.6247 - accuracy: 0.0000e+00\n",
      "Epoch 75/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 1895.0552 - accuracy: 0.0000e+00\n",
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      "Epoch 243/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 697.2145 - accuracy: 0.0079\n",
      "Epoch 244/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 689.4823 - accuracy: 0.0157\n",
      "Epoch 245/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 692.9177 - accuracy: 0.0079\n",
      "Epoch 246/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 688.6593 - accuracy: 0.0000e+00\n",
      "Epoch 247/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 689.7904 - accuracy: 0.0079\n",
      "Epoch 248/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 687.7958 - accuracy: 0.0079\n",
      "Epoch 249/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 689.4669 - accuracy: 0.0000e+00\n",
      "Epoch 250/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 686.7735 - accuracy: 0.0000e+00\n",
      "Epoch 251/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 683.4241 - accuracy: 0.0000e+00\n",
      "Epoch 252/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 683.4499 - accuracy: 0.0079\n",
      "Epoch 253/300\n",
      "127/127 [==============================] - 0s 40us/step - loss: 683.4112 - accuracy: 0.0079\n",
      "Epoch 254/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 687.1992 - accuracy: 0.0000e+00\n",
      "Epoch 255/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 679.1168 - accuracy: 0.0079\n",
      "Epoch 256/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 681.2994 - accuracy: 0.0079\n",
      "Epoch 257/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 675.9803 - accuracy: 0.0157\n",
      "Epoch 258/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 678.5187 - accuracy: 0.0079\n",
      "Epoch 259/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 682.2027 - accuracy: 0.0000e+00\n",
      "Epoch 260/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 680.5635 - accuracy: 0.0000e+00\n",
      "Epoch 261/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 675.4994 - accuracy: 0.0079\n",
      "Epoch 262/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 675.9496 - accuracy: 0.0000e+00\n",
      "Epoch 263/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 668.1201 - accuracy: 0.0000e+00\n",
      "Epoch 264/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 670.3465 - accuracy: 0.0000e+00\n",
      "Epoch 265/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 670.1746 - accuracy: 0.0000e+00\n",
      "Epoch 266/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 671.3024 - accuracy: 0.0000e+00\n",
      "Epoch 267/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 666.5088 - accuracy: 0.0000e+00\n",
      "Epoch 268/300\n",
      "127/127 [==============================] - 0s 40us/step - loss: 667.2748 - accuracy: 0.0079\n",
      "Epoch 269/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 669.8615 - accuracy: 0.0000e+00\n",
      "Epoch 270/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 668.8620 - accuracy: 0.0000e+00\n",
      "Epoch 271/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 666.8000 - accuracy: 0.0000e+00\n",
      "Epoch 272/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 667.8281 - accuracy: 0.0079\n",
      "Epoch 273/300\n",
      "127/127 [==============================] - 0s 40us/step - loss: 662.3064 - accuracy: 0.0000e+00\n",
      "Epoch 274/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 664.1928 - accuracy: 0.0157\n",
      "Epoch 275/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 667.3129 - accuracy: 0.0000e+00\n",
      "Epoch 276/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 665.7703 - accuracy: 0.0000e+00\n",
      "Epoch 277/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 660.4353 - accuracy: 0.0079\n",
      "Epoch 278/300\n",
      "127/127 [==============================] - 0s 40us/step - loss: 664.6700 - accuracy: 0.0000e+00\n",
      "Epoch 279/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 660.3897 - accuracy: 0.0079\n",
      "Epoch 280/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 656.2671 - accuracy: 0.0000e+00\n",
      "Epoch 281/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 655.2520 - accuracy: 0.0000e+00\n",
      "Epoch 282/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 651.7758 - accuracy: 0.0000e+00\n",
      "Epoch 283/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 650.2389 - accuracy: 0.0157\n",
      "Epoch 284/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 653.3809 - accuracy: 0.0079\n",
      "Epoch 285/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 654.3572 - accuracy: 0.0079\n",
      "Epoch 286/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 651.8457 - accuracy: 0.0000e+00\n",
      "Epoch 287/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 653.0314 - accuracy: 0.0000e+00\n",
      "Epoch 288/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 647.5733 - accuracy: 0.0079\n",
      "Epoch 289/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 644.5194 - accuracy: 0.0000e+00\n",
      "Epoch 290/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 644.0465 - accuracy: 0.0157\n",
      "Epoch 291/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 645.3872 - accuracy: 0.0157\n",
      "Epoch 292/300\n",
      "127/127 [==============================] - 0s 40us/step - loss: 641.4270 - accuracy: 0.0000e+00\n",
      "Epoch 293/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 642.8889 - accuracy: 0.0000e+00\n",
      "Epoch 294/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 639.7081 - accuracy: 0.0079\n",
      "Epoch 295/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 641.0789 - accuracy: 0.0079\n",
      "Epoch 296/300\n",
      "127/127 [==============================] - 0s 55us/step - loss: 637.2398 - accuracy: 0.0000e+00\n",
      "Epoch 297/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 635.5057 - accuracy: 0.0000e+00\n",
      "Epoch 298/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 636.6885 - accuracy: 0.0000e+00\n",
      "Epoch 299/300\n",
      "127/127 [==============================] - 0s 47us/step - loss: 637.2298 - accuracy: 0.0000e+00\n",
      "Epoch 300/300\n",
      "127/127 [==============================] - 0s 39us/step - loss: 633.5431 - accuracy: 0.0000e+00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x2646335f708>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = create_model(num_layers=grid_result.best_params_['num_layers'], layer_size=grid_result.best_params_['layer_size'], lr=grid_result.best_params_['lr'])\n",
    "model.fit(X_train, y_train, epochs=grid_result.best_params_['epochs'], batch_size=grid_result.best_params_['batch_size'], verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting and calculating MAE for test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1686.5008087158203"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "preds = model.predict(X_test)\n",
    "mean_absolute_error(y_test, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comment: MAE of ~1670 is similair to most of the results of estimators for this dataset found online"
   ]
  }
 ],
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