MLP  for binary classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## mlp for binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORT PACKAGES\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data \n",
    "\n",
    "We will use the Ionosphere binary (two-class) classification dataset to demonstrate an MLP for binary classification.\n",
    "\n",
    "This dataset involves predicting whether a structure is in the atmosphere or not given radar returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# load the dataset\n",
    "path = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/ionosphere.csv'\n",
    "df = read_csv(path, header=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(351, 35)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data shape\n",
    "df.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
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       "      <td>b</td>\n",
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       "      <td>1.00000</td>\n",
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       "      <td>0.01198</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.40220</td>\n",
       "      <td>0.58984</td>\n",
       "      <td>-0.22145</td>\n",
       "      <td>0.43100</td>\n",
       "      <td>-0.17365</td>\n",
       "      <td>0.60436</td>\n",
       "      <td>-0.24180</td>\n",
       "      <td>0.56045</td>\n",
       "      <td>-0.38238</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.45161</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.90695</td>\n",
       "      <td>0.51613</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.20099</td>\n",
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       "      <td>1.00000</td>\n",
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       "      <td>1.00000</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.02401</td>\n",
       "      <td>0.94140</td>\n",
       "      <td>0.06531</td>\n",
       "      <td>0.92106</td>\n",
       "      <td>-0.23255</td>\n",
       "      <td>0.77152</td>\n",
       "      <td>-0.16399</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.65158</td>\n",
       "      <td>0.13290</td>\n",
       "      <td>-0.53206</td>\n",
       "      <td>0.02431</td>\n",
       "      <td>-0.62197</td>\n",
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       "      <td>-0.04608</td>\n",
       "      <td>-0.65697</td>\n",
       "      <td>g</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 35 columns</p>\n",
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       "   0   1        2        3        4        5        6        7        8   \\\n",
       "0   1   0  0.99539 -0.05889  0.85243  0.02306  0.83398 -0.37708  1.00000   \n",
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       "4   1   0  1.00000 -0.02401  0.94140  0.06531  0.92106 -0.23255  0.77152   \n",
       "\n",
       "        9   ...       25       26       27       28       29       30  \\\n",
       "0  0.03760  ... -0.51171  0.41078 -0.46168  0.21266 -0.34090  0.42267   \n",
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       "3  0.00000  ...  0.90695  0.51613  1.00000  1.00000 -0.20099  0.25682   \n",
       "4 -0.16399  ... -0.65158  0.13290 -0.53206  0.02431 -0.62197 -0.05707   \n",
       "\n",
       "        31       32       33  34  \n",
       "0 -0.54487  0.18641 -0.45300   g  \n",
       "1 -0.06288 -0.13738 -0.02447   b  \n",
       "2 -0.24180  0.56045 -0.38238   g  \n",
       "3  1.00000 -0.32382  1.00000   b  \n",
       "4 -0.59573 -0.04608 -0.65697   g  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the top five rows\n",
    "df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "5     0\n",
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       "7     0\n",
       "8     0\n",
       "9     0\n",
       "10    0\n",
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       "dtype: int64"
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "#check missing data\n",
    "df.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We dont have any missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-1.0</td>\n",
       "      <td>0.472135</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>351.0</td>\n",
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       "      <td>-1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.601068</td>\n",
       "      <td>0.519862</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.412660</td>\n",
       "      <td>0.80920</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>351.0</td>\n",
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       "      <td>0.460810</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.024795</td>\n",
       "      <td>0.02280</td>\n",
       "      <td>0.334655</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.550095</td>\n",
       "      <td>0.492654</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.211310</td>\n",
       "      <td>0.72873</td>\n",
       "      <td>0.969240</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.119360</td>\n",
       "      <td>0.520750</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.054840</td>\n",
       "      <td>0.01471</td>\n",
       "      <td>0.445675</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.511848</td>\n",
       "      <td>0.507066</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.087110</td>\n",
       "      <td>0.68421</td>\n",
       "      <td>0.953240</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.181345</td>\n",
       "      <td>0.483851</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.048075</td>\n",
       "      <td>0.01829</td>\n",
       "      <td>0.534195</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.476183</td>\n",
       "      <td>0.563496</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.021120</td>\n",
       "      <td>0.66798</td>\n",
       "      <td>0.957895</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.155040</td>\n",
       "      <td>0.494817</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.065265</td>\n",
       "      <td>0.02825</td>\n",
       "      <td>0.482375</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>351.0</td>\n",
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       "      <td>-1.0</td>\n",
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       "      <td>351.0</td>\n",
       "      <td>0.093414</td>\n",
       "      <td>0.494873</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.073725</td>\n",
       "      <td>0.03027</td>\n",
       "      <td>0.374860</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.344159</td>\n",
       "      <td>0.652828</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.60194</td>\n",
       "      <td>0.919330</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.071132</td>\n",
       "      <td>0.458371</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.081705</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.308975</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.381949</td>\n",
       "      <td>0.618020</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.59091</td>\n",
       "      <td>0.935705</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>351.0</td>\n",
       "      <td>-0.003617</td>\n",
       "      <td>0.496762</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.225690</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.195285</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.359390</td>\n",
       "      <td>0.626267</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.57619</td>\n",
       "      <td>0.899265</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
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       "      <td>-0.024025</td>\n",
       "      <td>0.519076</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.234670</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.134370</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.336695</td>\n",
       "      <td>0.609828</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.49909</td>\n",
       "      <td>0.894865</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.008296</td>\n",
       "      <td>0.518166</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.243870</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.188760</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.362475</td>\n",
       "      <td>0.603767</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.53176</td>\n",
       "      <td>0.911235</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>351.0</td>\n",
       "      <td>-0.057406</td>\n",
       "      <td>0.527456</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.366885</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.164630</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.396135</td>\n",
       "      <td>0.578451</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.55389</td>\n",
       "      <td>0.905240</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>351.0</td>\n",
       "      <td>-0.071187</td>\n",
       "      <td>0.508495</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.332390</td>\n",
       "      <td>-0.01505</td>\n",
       "      <td>0.156765</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.541641</td>\n",
       "      <td>0.516205</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.286435</td>\n",
       "      <td>0.70824</td>\n",
       "      <td>0.999945</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>351.0</td>\n",
       "      <td>-0.069538</td>\n",
       "      <td>0.550025</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.443165</td>\n",
       "      <td>-0.01769</td>\n",
       "      <td>0.153535</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.378445</td>\n",
       "      <td>0.575886</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.49664</td>\n",
       "      <td>0.883465</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>351.0</td>\n",
       "      <td>-0.027907</td>\n",
       "      <td>0.507974</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.236885</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.154075</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.352514</td>\n",
       "      <td>0.571483</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.44277</td>\n",
       "      <td>0.857620</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>351.0</td>\n",
       "      <td>-0.003794</td>\n",
       "      <td>0.513574</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.242595</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.200120</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.349364</td>\n",
       "      <td>0.522663</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.40956</td>\n",
       "      <td>0.813765</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.014480</td>\n",
       "      <td>0.468337</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.165350</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.171660</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count      mean       std  min       25%      50%       75%  max\n",
       "0   351.0  0.891738  0.311155  0.0  1.000000  1.00000  1.000000  1.0\n",
       "1   351.0  0.000000  0.000000  0.0  0.000000  0.00000  0.000000  0.0\n",
       "2   351.0  0.641342  0.497708 -1.0  0.472135  0.87111  1.000000  1.0\n",
       "3   351.0  0.044372  0.441435 -1.0 -0.064735  0.01631  0.194185  1.0\n",
       "4   351.0  0.601068  0.519862 -1.0  0.412660  0.80920  1.000000  1.0\n",
       "5   351.0  0.115889  0.460810 -1.0 -0.024795  0.02280  0.334655  1.0\n",
       "6   351.0  0.550095  0.492654 -1.0  0.211310  0.72873  0.969240  1.0\n",
       "7   351.0  0.119360  0.520750 -1.0 -0.054840  0.01471  0.445675  1.0\n",
       "8   351.0  0.511848  0.507066 -1.0  0.087110  0.68421  0.953240  1.0\n",
       "9   351.0  0.181345  0.483851 -1.0 -0.048075  0.01829  0.534195  1.0\n",
       "10  351.0  0.476183  0.563496 -1.0  0.021120  0.66798  0.957895  1.0\n",
       "11  351.0  0.155040  0.494817 -1.0 -0.065265  0.02825  0.482375  1.0\n",
       "12  351.0  0.400801  0.622186 -1.0  0.000000  0.64407  0.955505  1.0\n",
       "13  351.0  0.093414  0.494873 -1.0 -0.073725  0.03027  0.374860  1.0\n",
       "14  351.0  0.344159  0.652828 -1.0  0.000000  0.60194  0.919330  1.0\n",
       "15  351.0  0.071132  0.458371 -1.0 -0.081705  0.00000  0.308975  1.0\n",
       "16  351.0  0.381949  0.618020 -1.0  0.000000  0.59091  0.935705  1.0\n",
       "17  351.0 -0.003617  0.496762 -1.0 -0.225690  0.00000  0.195285  1.0\n",
       "18  351.0  0.359390  0.626267 -1.0  0.000000  0.57619  0.899265  1.0\n",
       "19  351.0 -0.024025  0.519076 -1.0 -0.234670  0.00000  0.134370  1.0\n",
       "20  351.0  0.336695  0.609828 -1.0  0.000000  0.49909  0.894865  1.0\n",
       "21  351.0  0.008296  0.518166 -1.0 -0.243870  0.00000  0.188760  1.0\n",
       "22  351.0  0.362475  0.603767 -1.0  0.000000  0.53176  0.911235  1.0\n",
       "23  351.0 -0.057406  0.527456 -1.0 -0.366885  0.00000  0.164630  1.0\n",
       "24  351.0  0.396135  0.578451 -1.0  0.000000  0.55389  0.905240  1.0\n",
       "25  351.0 -0.071187  0.508495 -1.0 -0.332390 -0.01505  0.156765  1.0\n",
       "26  351.0  0.541641  0.516205 -1.0  0.286435  0.70824  0.999945  1.0\n",
       "27  351.0 -0.069538  0.550025 -1.0 -0.443165 -0.01769  0.153535  1.0\n",
       "28  351.0  0.378445  0.575886 -1.0  0.000000  0.49664  0.883465  1.0\n",
       "29  351.0 -0.027907  0.507974 -1.0 -0.236885  0.00000  0.154075  1.0\n",
       "30  351.0  0.352514  0.571483 -1.0  0.000000  0.44277  0.857620  1.0\n",
       "31  351.0 -0.003794  0.513574 -1.0 -0.242595  0.00000  0.200120  1.0\n",
       "32  351.0  0.349364  0.522663 -1.0  0.000000  0.40956  0.813765  1.0\n",
       "33  351.0  0.014480  0.468337 -1.0 -0.165350  0.00000  0.171660  1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check statistical properties \n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into input and output columns\n",
    "X, y = df.values[:, :-1], df.values[:, -1]\n",
    "\n",
    "# ensure all data are floating point values\n",
    "X = X.astype(\"float32\")\n",
    "\n",
    "# encode strings to integer\n",
    "y = LabelEncoder().fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(235, 34) (116, 34) (235,) (116,)\n"
     ]
    }
   ],
   "source": [
    "# split into train and test datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n",
    "# determine the number of input features\n",
    "n_features = X_train.shape[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define model\n",
    "model = Sequential()\n",
    "model.add(Dense(10, activation='relu', kernel_initializer='he_normal', input_shape=(n_features,)))\n",
    "model.add(Dense(8, activation='relu', kernel_initializer='he_normal'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile the model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 235 samples\n",
      "Epoch 1/150\n",
      "235/235 - 0s - loss: 0.0486 - accuracy: 0.9872\n",
      "Epoch 2/150\n",
      "235/235 - 0s - loss: 0.0480 - accuracy: 0.9872\n",
      "Epoch 3/150\n",
      "235/235 - 0s - loss: 0.0477 - accuracy: 0.9872\n",
      "Epoch 4/150\n",
      "235/235 - 0s - loss: 0.0471 - accuracy: 0.9872\n",
      "Epoch 5/150\n",
      "235/235 - 0s - loss: 0.0467 - accuracy: 0.9872\n",
      "Epoch 6/150\n",
      "235/235 - 0s - loss: 0.0465 - accuracy: 0.9872\n",
      "Epoch 7/150\n",
      "235/235 - 0s - loss: 0.0463 - accuracy: 0.9872\n",
      "Epoch 8/150\n",
      "235/235 - 0s - loss: 0.0452 - accuracy: 0.9872\n",
      "Epoch 9/150\n",
      "235/235 - 0s - loss: 0.0453 - accuracy: 0.9915\n",
      "Epoch 10/150\n",
      "235/235 - 0s - loss: 0.0450 - accuracy: 0.9915\n",
      "Epoch 11/150\n",
      "235/235 - 0s - loss: 0.0443 - accuracy: 0.9915\n",
      "Epoch 12/150\n",
      "235/235 - 0s - loss: 0.0442 - accuracy: 0.9915\n",
      "Epoch 13/150\n",
      "235/235 - 0s - loss: 0.0437 - accuracy: 0.9915\n",
      "Epoch 14/150\n",
      "235/235 - 0s - loss: 0.0435 - accuracy: 0.9872\n",
      "Epoch 15/150\n",
      "235/235 - 0s - loss: 0.0429 - accuracy: 0.9915\n",
      "Epoch 16/150\n",
      "235/235 - 0s - loss: 0.0425 - accuracy: 0.9915\n",
      "Epoch 17/150\n",
      "235/235 - 0s - loss: 0.0422 - accuracy: 0.9915\n",
      "Epoch 18/150\n",
      "235/235 - 0s - loss: 0.0419 - accuracy: 0.9915\n",
      "Epoch 19/150\n",
      "235/235 - 0s - loss: 0.0415 - accuracy: 0.9915\n",
      "Epoch 20/150\n",
      "235/235 - 0s - loss: 0.0413 - accuracy: 0.9915\n",
      "Epoch 21/150\n",
      "235/235 - 0s - loss: 0.0408 - accuracy: 0.9915\n",
      "Epoch 22/150\n",
      "235/235 - 0s - loss: 0.0407 - accuracy: 0.9915\n",
      "Epoch 23/150\n",
      "235/235 - 0s - loss: 0.0403 - accuracy: 0.9915\n",
      "Epoch 24/150\n",
      "235/235 - 0s - loss: 0.0400 - accuracy: 0.9915\n",
      "Epoch 25/150\n",
      "235/235 - 0s - loss: 0.0397 - accuracy: 0.9915\n",
      "Epoch 26/150\n",
      "235/235 - 0s - loss: 0.0393 - accuracy: 0.9915\n",
      "Epoch 27/150\n",
      "235/235 - 0s - loss: 0.0389 - accuracy: 0.9957\n",
      "Epoch 28/150\n",
      "235/235 - 0s - loss: 0.0388 - accuracy: 0.9957\n",
      "Epoch 29/150\n",
      "235/235 - 0s - loss: 0.0386 - accuracy: 0.9957\n",
      "Epoch 30/150\n",
      "235/235 - 0s - loss: 0.0380 - accuracy: 0.9915\n",
      "Epoch 31/150\n",
      "235/235 - 0s - loss: 0.0380 - accuracy: 0.9915\n",
      "Epoch 32/150\n",
      "235/235 - 0s - loss: 0.0382 - accuracy: 0.9915\n",
      "Epoch 33/150\n",
      "235/235 - 0s - loss: 0.0374 - accuracy: 0.9915\n",
      "Epoch 34/150\n",
      "235/235 - 0s - loss: 0.0371 - accuracy: 0.9915\n",
      "Epoch 35/150\n",
      "235/235 - 0s - loss: 0.0366 - accuracy: 0.9915\n",
      "Epoch 36/150\n",
      "235/235 - 0s - loss: 0.0367 - accuracy: 0.9957\n",
      "Epoch 37/150\n",
      "235/235 - 0s - loss: 0.0361 - accuracy: 0.9957\n",
      "Epoch 38/150\n",
      "235/235 - 0s - loss: 0.0360 - accuracy: 0.9957\n",
      "Epoch 39/150\n",
      "235/235 - 0s - loss: 0.0359 - accuracy: 0.9957\n",
      "Epoch 40/150\n",
      "235/235 - 0s - loss: 0.0356 - accuracy: 0.9957\n",
      "Epoch 41/150\n",
      "235/235 - 0s - loss: 0.0350 - accuracy: 0.9957\n",
      "Epoch 42/150\n",
      "235/235 - 0s - loss: 0.0349 - accuracy: 0.9957\n",
      "Epoch 43/150\n",
      "235/235 - 0s - loss: 0.0345 - accuracy: 0.9957\n",
      "Epoch 44/150\n",
      "235/235 - 0s - loss: 0.0345 - accuracy: 0.9957\n",
      "Epoch 45/150\n",
      "235/235 - 0s - loss: 0.0345 - accuracy: 0.9957\n",
      "Epoch 46/150\n",
      "235/235 - 0s - loss: 0.0337 - accuracy: 0.9957\n",
      "Epoch 47/150\n",
      "235/235 - 0s - loss: 0.0337 - accuracy: 0.9957\n",
      "Epoch 48/150\n",
      "235/235 - 0s - loss: 0.0333 - accuracy: 0.9957\n",
      "Epoch 49/150\n",
      "235/235 - 0s - loss: 0.0331 - accuracy: 0.9957\n",
      "Epoch 50/150\n",
      "235/235 - 0s - loss: 0.0329 - accuracy: 0.9957\n",
      "Epoch 51/150\n",
      "235/235 - 0s - loss: 0.0325 - accuracy: 0.9957\n",
      "Epoch 52/150\n",
      "235/235 - 0s - loss: 0.0323 - accuracy: 0.9957\n",
      "Epoch 53/150\n",
      "235/235 - 0s - loss: 0.0324 - accuracy: 0.9957\n",
      "Epoch 54/150\n",
      "235/235 - 0s - loss: 0.0320 - accuracy: 0.9957\n",
      "Epoch 55/150\n",
      "235/235 - 0s - loss: 0.0319 - accuracy: 0.9957\n",
      "Epoch 56/150\n",
      "235/235 - 0s - loss: 0.0316 - accuracy: 0.9957\n",
      "Epoch 57/150\n",
      "235/235 - 0s - loss: 0.0312 - accuracy: 0.9957\n",
      "Epoch 58/150\n",
      "235/235 - 0s - loss: 0.0308 - accuracy: 0.9957\n",
      "Epoch 59/150\n",
      "235/235 - 0s - loss: 0.0306 - accuracy: 0.9957\n",
      "Epoch 60/150\n",
      "235/235 - 0s - loss: 0.0305 - accuracy: 0.9957\n",
      "Epoch 61/150\n",
      "235/235 - 0s - loss: 0.0304 - accuracy: 0.9957\n",
      "Epoch 62/150\n",
      "235/235 - 0s - loss: 0.0300 - accuracy: 0.9957\n",
      "Epoch 63/150\n",
      "235/235 - 0s - loss: 0.0297 - accuracy: 0.9957\n",
      "Epoch 64/150\n",
      "235/235 - 0s - loss: 0.0296 - accuracy: 0.9957\n",
      "Epoch 65/150\n",
      "235/235 - 0s - loss: 0.0294 - accuracy: 0.9957\n",
      "Epoch 66/150\n",
      "235/235 - 0s - loss: 0.0292 - accuracy: 0.9957\n",
      "Epoch 67/150\n",
      "235/235 - 0s - loss: 0.0290 - accuracy: 0.9957\n",
      "Epoch 68/150\n",
      "235/235 - 0s - loss: 0.0295 - accuracy: 0.9957\n",
      "Epoch 69/150\n",
      "235/235 - 0s - loss: 0.0292 - accuracy: 0.9957\n",
      "Epoch 70/150\n",
      "235/235 - 0s - loss: 0.0292 - accuracy: 0.9957\n",
      "Epoch 71/150\n",
      "235/235 - 0s - loss: 0.0289 - accuracy: 0.9957\n",
      "Epoch 72/150\n",
      "235/235 - 0s - loss: 0.0282 - accuracy: 0.9957\n",
      "Epoch 73/150\n",
      "235/235 - 0s - loss: 0.0284 - accuracy: 0.9957\n",
      "Epoch 74/150\n",
      "235/235 - 0s - loss: 0.0278 - accuracy: 0.9957\n",
      "Epoch 75/150\n",
      "235/235 - 0s - loss: 0.0276 - accuracy: 0.9957\n",
      "Epoch 76/150\n",
      "235/235 - 0s - loss: 0.0274 - accuracy: 0.9957\n",
      "Epoch 77/150\n",
      "235/235 - 0s - loss: 0.0271 - accuracy: 0.9957\n",
      "Epoch 78/150\n",
      "235/235 - 0s - loss: 0.0271 - accuracy: 0.9957\n",
      "Epoch 79/150\n",
      "235/235 - 0s - loss: 0.0269 - accuracy: 0.9957\n",
      "Epoch 80/150\n",
      "235/235 - 0s - loss: 0.0266 - accuracy: 0.9957\n",
      "Epoch 81/150\n",
      "235/235 - 0s - loss: 0.0268 - accuracy: 0.9957\n",
      "Epoch 82/150\n",
      "235/235 - 0s - loss: 0.0263 - accuracy: 0.9957\n",
      "Epoch 83/150\n",
      "235/235 - 0s - loss: 0.0262 - accuracy: 0.9957\n",
      "Epoch 84/150\n",
      "235/235 - 0s - loss: 0.0264 - accuracy: 0.9957\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 137/150\n",
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      "Epoch 138/150\n",
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      "Epoch 142/150\n",
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      "Epoch 143/150\n",
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      "Epoch 144/150\n",
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      "Epoch 145/150\n",
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      "Epoch 146/150\n",
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      "Epoch 147/150\n",
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      "Epoch 148/150\n",
      "235/235 - 0s - loss: 0.0191 - accuracy: 0.9957\n",
      "Epoch 149/150\n",
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      "Epoch 150/150\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d7563d3c50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the model\n",
    "model.fit(X_train, y_train, epochs=150, batch_size=32, verbose=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.922\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model\n",
    "loss, acc = model.evaluate(X_test, y_test, verbose=0)\n",
    "print('Test Accuracy: %.3f' % acc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 1.000\n"
     ]
    }
   ],
   "source": [
    "row = [1,0,0.99539,-0.05889,0.85243,0.02306,0.83398,-0.37708,1,0.03760,0.85243,-0.17755,0.59755,-0.44945,0.60536,-0.38223,0.84356,-0.38542,0.58212,-0.32192,0.56971,-0.29674,0.36946,-0.47357,0.56811,-0.51171,0.41078,-0.46168,0.21266,-0.34090,0.42267,-0.54487,0.18641,-0.45300]\n",
    "result = model.predict([row])\n",
    "print('Predicted: %.3f' % result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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