{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aa_000</th>\n",
       "      <th>ab_000</th>\n",
       "      <th>ac_000</th>\n",
       "      <th>ad_000</th>\n",
       "      <th>ae_000</th>\n",
       "      <th>af_000</th>\n",
       "      <th>ag_000</th>\n",
       "      <th>ag_001</th>\n",
       "      <th>ag_002</th>\n",
       "      <th>ag_003</th>\n",
       "      <th>...</th>\n",
       "      <th>ee_002</th>\n",
       "      <th>ee_003</th>\n",
       "      <th>ee_004</th>\n",
       "      <th>ee_005</th>\n",
       "      <th>ee_006</th>\n",
       "      <th>ee_007</th>\n",
       "      <th>ee_008</th>\n",
       "      <th>ee_009</th>\n",
       "      <th>ef_000</th>\n",
       "      <th>eg_000</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>76698</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.130706e+09</td>\n",
       "      <td>280.0</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>...</td>\n",
       "      <td>1240520.0</td>\n",
       "      <td>493384.0</td>\n",
       "      <td>721044.0</td>\n",
       "      <td>469792.0</td>\n",
       "      <td>339156.0</td>\n",
       "      <td>157956.0</td>\n",
       "      <td>73224.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</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>...</td>\n",
       "      <td>421400.0</td>\n",
       "      <td>178064.0</td>\n",
       "      <td>293306.0</td>\n",
       "      <td>245416.0</td>\n",
       "      <td>133654.0</td>\n",
       "      <td>81140.0</td>\n",
       "      <td>97576.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.280000e+02</td>\n",
       "      <td>100.0</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>...</td>\n",
       "      <td>277378.0</td>\n",
       "      <td>159812.0</td>\n",
       "      <td>423992.0</td>\n",
       "      <td>409564.0</td>\n",
       "      <td>320746.0</td>\n",
       "      <td>158022.0</td>\n",
       "      <td>95128.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368000e+03</td>\n",
       "      <td>458.0</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>...</td>\n",
       "      <td>622012.0</td>\n",
       "      <td>229790.0</td>\n",
       "      <td>405298.0</td>\n",
       "      <td>347188.0</td>\n",
       "      <td>286954.0</td>\n",
       "      <td>311560.0</td>\n",
       "      <td>433954.0</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 170 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   aa_000  ab_000        ac_000  ad_000  ae_000  af_000  ag_000  ag_001  \\\n",
       "0   76698     0.0  2.130706e+09   280.0     0.0     0.0     0.0     0.0   \n",
       "1   33058     0.0  0.000000e+00     0.0     0.0     0.0     0.0     0.0   \n",
       "2   41040     0.0  2.280000e+02   100.0     0.0     0.0     0.0     0.0   \n",
       "3      12     0.0  7.000000e+01    66.0     0.0    10.0     0.0     0.0   \n",
       "4   60874     0.0  1.368000e+03   458.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "   ag_002  ag_003  ...     ee_002    ee_003    ee_004    ee_005    ee_006  \\\n",
       "0     0.0     0.0  ...  1240520.0  493384.0  721044.0  469792.0  339156.0   \n",
       "1     0.0     0.0  ...   421400.0  178064.0  293306.0  245416.0  133654.0   \n",
       "2     0.0     0.0  ...   277378.0  159812.0  423992.0  409564.0  320746.0   \n",
       "3     0.0   318.0  ...      240.0      46.0      58.0      44.0      10.0   \n",
       "4     0.0     0.0  ...   622012.0  229790.0  405298.0  347188.0  286954.0   \n",
       "\n",
       "     ee_007    ee_008  ee_009  ef_000  eg_000  \n",
       "0  157956.0   73224.0     0.0     0.0     0.0  \n",
       "1   81140.0   97576.0  1500.0     0.0     0.0  \n",
       "2  158022.0   95128.0   514.0     0.0     0.0  \n",
       "3       0.0       0.0     0.0     4.0    32.0  \n",
       "4  311560.0  433954.0  1218.0     0.0     0.0  \n",
       "\n",
       "[5 rows x 170 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow import random\n",
    "\n",
    "# Load the Data\n",
    "X = pd.read_csv(\"../data/aps_failure_training_feats.csv\")\n",
    "y = pd.read_csv(\"../data/aps_failure_training_target.csv\")\n",
    "\n",
    "# use the head function view the first 5 rows of the data\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aa_000</th>\n",
       "      <th>ab_000</th>\n",
       "      <th>ac_000</th>\n",
       "      <th>ad_000</th>\n",
       "      <th>ae_000</th>\n",
       "      <th>af_000</th>\n",
       "      <th>ag_000</th>\n",
       "      <th>ag_001</th>\n",
       "      <th>ag_002</th>\n",
       "      <th>ag_003</th>\n",
       "      <th>...</th>\n",
       "      <th>ee_002</th>\n",
       "      <th>ee_003</th>\n",
       "      <th>ee_004</th>\n",
       "      <th>ee_005</th>\n",
       "      <th>ee_006</th>\n",
       "      <th>ee_007</th>\n",
       "      <th>ee_008</th>\n",
       "      <th>ee_009</th>\n",
       "      <th>ef_000</th>\n",
       "      <th>eg_000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.933650e+04</td>\n",
       "      <td>0.162500</td>\n",
       "      <td>3.362258e+08</td>\n",
       "      <td>1.434071e+05</td>\n",
       "      <td>6.535000</td>\n",
       "      <td>10.548200</td>\n",
       "      <td>2.191577e+02</td>\n",
       "      <td>9.648104e+02</td>\n",
       "      <td>8.509771e+03</td>\n",
       "      <td>8.760054e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>4.405077e+05</td>\n",
       "      <td>2.087653e+05</td>\n",
       "      <td>4.407495e+05</td>\n",
       "      <td>3.895406e+05</td>\n",
       "      <td>3.293335e+05</td>\n",
       "      <td>3.423990e+05</td>\n",
       "      <td>1.371785e+05</td>\n",
       "      <td>8.295099e+03</td>\n",
       "      <td>0.086467</td>\n",
       "      <td>0.203100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.454301e+05</td>\n",
       "      <td>1.687318</td>\n",
       "      <td>7.767625e+08</td>\n",
       "      <td>3.504525e+07</td>\n",
       "      <td>158.147893</td>\n",
       "      <td>205.387115</td>\n",
       "      <td>2.036364e+04</td>\n",
       "      <td>3.400891e+04</td>\n",
       "      <td>1.494818e+05</td>\n",
       "      <td>7.575171e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.150015e+06</td>\n",
       "      <td>5.407282e+05</td>\n",
       "      <td>1.162708e+06</td>\n",
       "      <td>1.115528e+06</td>\n",
       "      <td>1.063741e+06</td>\n",
       "      <td>1.718752e+06</td>\n",
       "      <td>4.472274e+05</td>\n",
       "      <td>4.721249e+04</td>\n",
       "      <td>4.268570</td>\n",
       "      <td>8.628043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.340000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.662000e+03</td>\n",
       "      <td>1.024000e+03</td>\n",
       "      <td>2.288000e+03</td>\n",
       "      <td>2.897000e+03</td>\n",
       "      <td>3.960000e+02</td>\n",
       "      <td>8.800000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.077600e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>4.200000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.280220e+05</td>\n",
       "      <td>1.088190e+05</td>\n",
       "      <td>2.153240e+05</td>\n",
       "      <td>1.838280e+05</td>\n",
       "      <td>8.491400e+04</td>\n",
       "      <td>3.840600e+04</td>\n",
       "      <td>3.276000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.866800e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.480000e+02</td>\n",
       "      <td>2.920000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>4.350315e+05</td>\n",
       "      <td>2.164440e+05</td>\n",
       "      <td>4.624655e+05</td>\n",
       "      <td>4.003010e+05</td>\n",
       "      <td>2.725220e+05</td>\n",
       "      <td>1.663465e+05</td>\n",
       "      <td>1.367255e+05</td>\n",
       "      <td>1.934000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.746564e+06</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>2.130707e+09</td>\n",
       "      <td>8.584298e+09</td>\n",
       "      <td>21050.000000</td>\n",
       "      <td>20070.000000</td>\n",
       "      <td>3.376892e+06</td>\n",
       "      <td>4.109372e+06</td>\n",
       "      <td>1.055286e+07</td>\n",
       "      <td>6.340207e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>7.793393e+07</td>\n",
       "      <td>3.775839e+07</td>\n",
       "      <td>9.715238e+07</td>\n",
       "      <td>5.743524e+07</td>\n",
       "      <td>3.160781e+07</td>\n",
       "      <td>1.195801e+08</td>\n",
       "      <td>1.926740e+07</td>\n",
       "      <td>3.810078e+06</td>\n",
       "      <td>482.000000</td>\n",
       "      <td>1146.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 170 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             aa_000        ab_000        ac_000        ad_000        ae_000  \\\n",
       "count  6.000000e+04  60000.000000  6.000000e+04  6.000000e+04  60000.000000   \n",
       "mean   5.933650e+04      0.162500  3.362258e+08  1.434071e+05      6.535000   \n",
       "std    1.454301e+05      1.687318  7.767625e+08  3.504525e+07    158.147893   \n",
       "min    0.000000e+00      0.000000  0.000000e+00  0.000000e+00      0.000000   \n",
       "25%    8.340000e+02      0.000000  8.000000e+00  0.000000e+00      0.000000   \n",
       "50%    3.077600e+04      0.000000  1.200000e+02  4.200000e+01      0.000000   \n",
       "75%    4.866800e+04      0.000000  8.480000e+02  2.920000e+02      0.000000   \n",
       "max    2.746564e+06    204.000000  2.130707e+09  8.584298e+09  21050.000000   \n",
       "\n",
       "             af_000        ag_000        ag_001        ag_002        ag_003  \\\n",
       "count  60000.000000  6.000000e+04  6.000000e+04  6.000000e+04  6.000000e+04   \n",
       "mean      10.548200  2.191577e+02  9.648104e+02  8.509771e+03  8.760054e+04   \n",
       "std      205.387115  2.036364e+04  3.400891e+04  1.494818e+05  7.575171e+05   \n",
       "min        0.000000  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%        0.000000  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%        0.000000  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%        0.000000  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "max    20070.000000  3.376892e+06  4.109372e+06  1.055286e+07  6.340207e+07   \n",
       "\n",
       "       ...        ee_002        ee_003        ee_004        ee_005  \\\n",
       "count  ...  6.000000e+04  6.000000e+04  6.000000e+04  6.000000e+04   \n",
       "mean   ...  4.405077e+05  2.087653e+05  4.407495e+05  3.895406e+05   \n",
       "std    ...  1.150015e+06  5.407282e+05  1.162708e+06  1.115528e+06   \n",
       "min    ...  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    ...  2.662000e+03  1.024000e+03  2.288000e+03  2.897000e+03   \n",
       "50%    ...  2.280220e+05  1.088190e+05  2.153240e+05  1.838280e+05   \n",
       "75%    ...  4.350315e+05  2.164440e+05  4.624655e+05  4.003010e+05   \n",
       "max    ...  7.793393e+07  3.775839e+07  9.715238e+07  5.743524e+07   \n",
       "\n",
       "             ee_006        ee_007        ee_008        ee_009        ef_000  \\\n",
       "count  6.000000e+04  6.000000e+04  6.000000e+04  6.000000e+04  60000.000000   \n",
       "mean   3.293335e+05  3.423990e+05  1.371785e+05  8.295099e+03      0.086467   \n",
       "std    1.063741e+06  1.718752e+06  4.472274e+05  4.721249e+04      4.268570   \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00      0.000000   \n",
       "25%    3.960000e+02  8.800000e+01  0.000000e+00  0.000000e+00      0.000000   \n",
       "50%    8.491400e+04  3.840600e+04  3.276000e+03  0.000000e+00      0.000000   \n",
       "75%    2.725220e+05  1.663465e+05  1.367255e+05  1.934000e+03      0.000000   \n",
       "max    3.160781e+07  1.195801e+08  1.926740e+07  3.810078e+06    482.000000   \n",
       "\n",
       "             eg_000  \n",
       "count  60000.000000  \n",
       "mean       0.203100  \n",
       "std        8.628043  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max     1146.000000  \n",
       "\n",
       "[8 rows x 170 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of Numerical Data\n",
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
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      "text/plain": [
       "   class\n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "seed = 42\n",
    "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size=0.20, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "\n",
    "# Transform the training data\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_train = pd.DataFrame(X_train,columns=X_test.columns)\n",
    "\n",
    "# Transform the testing data\n",
    "X_test=sc.transform(X_test)\n",
    "X_test=pd.DataFrame(X_test,columns=X_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train a Neural Network and find its accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import the relevant Keras libraries\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initiate the model with sequential class\n",
    "np.random.seed(seed)\n",
    "random.set_seed(seed)\n",
    "model=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the hidden dense layers and with dropout Layer\n",
    "model.add(Dense(units=64, activation='relu', kernel_initializer='uniform', input_dim=X_train.shape[1]))\n",
    "model.add(Dropout(rate=0.5))\n",
    "model.add(Dense(units=32, activation='relu', kernel_initializer='uniform', input_dim=X_train.shape[1]))\n",
    "model.add(Dropout(rate=0.4))\n",
    "model.add(Dense(units=16, activation='relu', kernel_initializer='uniform', input_dim=X_train.shape[1]))\n",
    "model.add(Dropout(rate=0.3))\n",
    "model.add(Dense(units=8, activation='relu', kernel_initializer='uniform', input_dim=X_train.shape[1]))\n",
    "model.add(Dropout(rate=0.2))\n",
    "model.add(Dense(units=4, activation='relu', kernel_initializer='uniform'))\n",
    "model.add(Dropout(rate=0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Output Dense Layer\n",
    "model.add(Dense(units=1, activation='sigmoid', kernel_initializer='uniform'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the Model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 38400 samples, validate on 9600 samples\n",
      "Epoch 1/100\n",
      "38400/38400 [==============================] - 5s 139us/step - loss: 0.0805 - accuracy: 0.9833 - val_loss: 0.0365 - val_accuracy: 0.9842\n",
      "Epoch 2/100\n",
      "38400/38400 [==============================] - 5s 122us/step - loss: 0.0424 - accuracy: 0.9834 - val_loss: 0.0348 - val_accuracy: 0.9842\n",
      "Epoch 3/100\n",
      "38400/38400 [==============================] - 4s 107us/step - loss: 0.0381 - accuracy: 0.9834 - val_loss: 0.0321 - val_accuracy: 0.9842\n",
      "Epoch 4/100\n",
      "38400/38400 [==============================] - 4s 108us/step - loss: 0.0350 - accuracy: 0.9834 - val_loss: 0.0316 - val_accuracy: 0.9842\n",
      "Epoch 5/100\n",
      "38400/38400 [==============================] - 4s 106us/step - loss: 0.0360 - accuracy: 0.9838 - val_loss: 0.0288 - val_accuracy: 0.9904\n",
      "Epoch 6/100\n",
      "38400/38400 [==============================] - 5s 129us/step - loss: 0.0333 - accuracy: 0.9904 - val_loss: 0.0284 - val_accuracy: 0.9907\n",
      "Epoch 7/100\n",
      "38400/38400 [==============================] - 4s 115us/step - loss: 0.0332 - accuracy: 0.9902 - val_loss: 0.0290 - val_accuracy: 0.9901\n",
      "Epoch 8/100\n",
      "38400/38400 [==============================] - 4s 116us/step - loss: 0.0309 - accuracy: 0.9905 - val_loss: 0.0305 - val_accuracy: 0.9915\n",
      "Epoch 9/100\n",
      "38400/38400 [==============================] - 5s 117us/step - loss: 0.0312 - accuracy: 0.9908 - val_loss: 0.0291 - val_accuracy: 0.9900\n",
      "Epoch 10/100\n",
      "38400/38400 [==============================] - 4s 115us/step - loss: 0.0308 - accuracy: 0.9912 - val_loss: 0.0288 - val_accuracy: 0.9911\n",
      "Epoch 11/100\n",
      "38400/38400 [==============================] - 4s 106us/step - loss: 0.0293 - accuracy: 0.9908 - val_loss: 0.0276 - val_accuracy: 0.9915\n",
      "Epoch 12/100\n",
      "38400/38400 [==============================] - 4s 104us/step - loss: 0.0287 - accuracy: 0.9911 - val_loss: 0.0271 - val_accuracy: 0.9915\n",
      "Epoch 13/100\n",
      "38400/38400 [==============================] - 4s 102us/step - loss: 0.0281 - accuracy: 0.9913 - val_loss: 0.0337 - val_accuracy: 0.9902\n",
      "Epoch 14/100\n",
      "38400/38400 [==============================] - 4s 102us/step - loss: 0.0292 - accuracy: 0.9915 - val_loss: 0.0323 - val_accuracy: 0.9908\n",
      "Epoch 15/100\n",
      "38400/38400 [==============================] - 4s 103us/step - loss: 0.0289 - accuracy: 0.9921 - val_loss: 0.0270 - val_accuracy: 0.9932\n",
      "Epoch 16/100\n",
      "38400/38400 [==============================] - 4s 108us/step - loss: 0.0282 - accuracy: 0.9916 - val_loss: 0.0282 - val_accuracy: 0.9917\n",
      "Epoch 17/100\n",
      "38400/38400 [==============================] - 4s 105us/step - loss: 0.0285 - accuracy: 0.9922 - val_loss: 0.0326 - val_accuracy: 0.9915\n",
      "Epoch 18/100\n",
      "38400/38400 [==============================] - 4s 109us/step - loss: 0.0276 - accuracy: 0.9925 - val_loss: 0.0311 - val_accuracy: 0.9920\n",
      "Epoch 19/100\n",
      "38400/38400 [==============================] - 4s 108us/step - loss: 0.0267 - accuracy: 0.9921 - val_loss: 0.0314 - val_accuracy: 0.9915\n",
      "Epoch 20/100\n",
      "38400/38400 [==============================] - 4s 108us/step - loss: 0.0269 - accuracy: 0.9919 - val_loss: 0.0285 - val_accuracy: 0.9924\n",
      "Epoch 21/100\n",
      "38400/38400 [==============================] - 4s 110us/step - loss: 0.0281 - accuracy: 0.9920 - val_loss: 0.0313 - val_accuracy: 0.9917\n",
      "Epoch 22/100\n",
      "38400/38400 [==============================] - 4s 108us/step - loss: 0.0277 - accuracy: 0.9924 - val_loss: 0.0289 - val_accuracy: 0.9923\n",
      "Epoch 23/100\n",
      "38400/38400 [==============================] - 5s 121us/step - loss: 0.0274 - accuracy: 0.9924 - val_loss: 0.0293 - val_accuracy: 0.9924\n",
      "Epoch 24/100\n",
      "38400/38400 [==============================] - 4s 104us/step - loss: 0.0265 - accuracy: 0.9927 - val_loss: 0.0328 - val_accuracy: 0.9922\n",
      "Epoch 25/100\n",
      "38400/38400 [==============================] - 4s 96us/step - loss: 0.0257 - accuracy: 0.9929 - val_loss: 0.0326 - val_accuracy: 0.9922\n",
      "Epoch 26/100\n",
      "38400/38400 [==============================] - 4s 98us/step - loss: 0.0267 - accuracy: 0.9927 - val_loss: 0.0289 - val_accuracy: 0.9916\n",
      "Epoch 27/100\n",
      "38400/38400 [==============================] - 4s 102us/step - loss: 0.0249 - accuracy: 0.9931 - val_loss: 0.0316 - val_accuracy: 0.9921\n",
      "Epoch 28/100\n",
      "38400/38400 [==============================] - 4s 105us/step - loss: 0.0269 - accuracy: 0.9928 - val_loss: 0.0348 - val_accuracy: 0.9929\n",
      "Epoch 29/100\n",
      "38400/38400 [==============================] - 4s 92us/step - loss: 0.0246 - accuracy: 0.9927 - val_loss: 0.0318 - val_accuracy: 0.9927\n",
      "Epoch 30/100\n",
      "38400/38400 [==============================] - 4s 96us/step - loss: 0.0258 - accuracy: 0.9924 - val_loss: 0.0299 - val_accuracy: 0.9917\n",
      "Epoch 31/100\n",
      "38400/38400 [==============================] - 4s 95us/step - loss: 0.0250 - accuracy: 0.9928 - val_loss: 0.0286 - val_accuracy: 0.9912\n",
      "Epoch 32/100\n",
      "38400/38400 [==============================] - 4s 101us/step - loss: 0.0260 - accuracy: 0.9930 - val_loss: 0.0273 - val_accuracy: 0.9919\n",
      "Epoch 33/100\n",
      "38400/38400 [==============================] - 4s 103us/step - loss: 0.0239 - accuracy: 0.9933 - val_loss: 0.0336 - val_accuracy: 0.9919\n",
      "Epoch 34/100\n",
      "38400/38400 [==============================] - 4s 105us/step - loss: 0.0248 - accuracy: 0.9930 - val_loss: 0.0339 - val_accuracy: 0.9918\n",
      "Epoch 35/100\n",
      "38400/38400 [==============================] - 4s 100us/step - loss: 0.0255 - accuracy: 0.9932 - val_loss: 0.0326 - val_accuracy: 0.9919\n",
      "Epoch 36/100\n",
      "38400/38400 [==============================] - 4s 100us/step - loss: 0.0224 - accuracy: 0.9934 - val_loss: 0.0308 - val_accuracy: 0.9918\n",
      "Epoch 37/100\n",
      "38400/38400 [==============================] - 4s 102us/step - loss: 0.0239 - accuracy: 0.9934 - val_loss: 0.0337 - val_accuracy: 0.9921\n",
      "Epoch 38/100\n",
      "38400/38400 [==============================] - 4s 96us/step - loss: 0.0245 - accuracy: 0.9930 - val_loss: 0.0319 - val_accuracy: 0.9918\n",
      "Epoch 39/100\n",
      "38400/38400 [==============================] - 4s 96us/step - loss: 0.0242 - accuracy: 0.9936 - val_loss: 0.0323 - val_accuracy: 0.9931\n",
      "Epoch 40/100\n",
      "38400/38400 [==============================] - 4s 100us/step - loss: 0.0219 - accuracy: 0.9935 - val_loss: 0.0321 - val_accuracy: 0.9925\n",
      "Epoch 41/100\n",
      "38400/38400 [==============================] - 4s 115us/step - loss: 0.0244 - accuracy: 0.9939 - val_loss: 0.0364 - val_accuracy: 0.9930\n",
      "Epoch 42/100\n",
      "38400/38400 [==============================] - 4s 109us/step - loss: 0.0218 - accuracy: 0.9943 - val_loss: 0.0388 - val_accuracy: 0.9923\n",
      "Epoch 43/100\n",
      "38400/38400 [==============================] - 4s 109us/step - loss: 0.0241 - accuracy: 0.9935 - val_loss: 0.0321 - val_accuracy: 0.9926\n",
      "Epoch 44/100\n",
      "38400/38400 [==============================] - 4s 112us/step - loss: 0.0236 - accuracy: 0.9937 - val_loss: 0.0360 - val_accuracy: 0.9924\n",
      "Epoch 45/100\n",
      "38400/38400 [==============================] - 5s 130us/step - loss: 0.0223 - accuracy: 0.9937 - val_loss: 0.0396 - val_accuracy: 0.9928\n",
      "Epoch 46/100\n",
      "38400/38400 [==============================] - 5s 129us/step - loss: 0.0233 - accuracy: 0.9937 - val_loss: 0.0331 - val_accuracy: 0.9917\n",
      "Epoch 47/100\n",
      "38400/38400 [==============================] - 5s 139us/step - loss: 0.0221 - accuracy: 0.9938 - val_loss: 0.0358 - val_accuracy: 0.9923\n",
      "Epoch 48/100\n",
      "38400/38400 [==============================] - 6s 157us/step - loss: 0.0231 - accuracy: 0.9939 - val_loss: 0.0384 - val_accuracy: 0.9920\n",
      "Epoch 49/100\n",
      "38400/38400 [==============================] - 5s 140us/step - loss: 0.0217 - accuracy: 0.9941 - val_loss: 0.0367 - val_accuracy: 0.9916\n",
      "Epoch 50/100\n",
      "38400/38400 [==============================] - 6s 149us/step - loss: 0.0218 - accuracy: 0.9940 - val_loss: 0.0398 - val_accuracy: 0.9924 - loss: 0.0218 - accuracy: 0.99 - ETA: 0s - loss: 0.0219 - accuracy: 0.\n",
      "Epoch 51/100\n",
      "38400/38400 [==============================] - 6s 150us/step - loss: 0.0220 - accuracy: 0.9940 - val_loss: 0.0385 - val_accuracy: 0.9923\n",
      "Epoch 52/100\n",
      "38400/38400 [==============================] - 6s 149us/step - loss: 0.0227 - accuracy: 0.9941 - val_loss: 0.0376 - val_accuracy: 0.9910\n",
      "Epoch 53/100\n",
      "38400/38400 [==============================] - 6s 149us/step - loss: 0.0239 - accuracy: 0.9936 - val_loss: 0.0412 - val_accuracy: 0.9908\n",
      "Epoch 54/100\n",
      "38400/38400 [==============================] - 6s 154us/step - loss: 0.0230 - accuracy: 0.9943 - val_loss: 0.0455 - val_accuracy: 0.9920\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 55/100\n",
      "38400/38400 [==============================] - 5s 142us/step - loss: 0.0221 - accuracy: 0.9936 - val_loss: 0.0391 - val_accuracy: 0.9920\n",
      "Epoch 56/100\n",
      "38400/38400 [==============================] - 5s 142us/step - loss: 0.0216 - accuracy: 0.9942 - val_loss: 0.0426 - val_accuracy: 0.9923\n",
      "Epoch 57/100\n",
      "38400/38400 [==============================] - 6s 166us/step - loss: 0.0218 - accuracy: 0.9933 - val_loss: 0.0418 - val_accuracy: 0.9917\n",
      "Epoch 58/100\n",
      "38400/38400 [==============================] - 6s 144us/step - loss: 0.0235 - accuracy: 0.9937 - val_loss: 0.0414 - val_accuracy: 0.9921\n",
      "Epoch 59/100\n",
      "38400/38400 [==============================] - 6s 145us/step - loss: 0.0209 - accuracy: 0.9943 - val_loss: 0.0376 - val_accuracy: 0.9922\n",
      "Epoch 60/100\n",
      "38400/38400 [==============================] - 5s 126us/step - loss: 0.0246 - accuracy: 0.9940 - val_loss: 0.0441 - val_accuracy: 0.9918\n",
      "Epoch 61/100\n",
      "38400/38400 [==============================] - 5s 140us/step - loss: 0.0236 - accuracy: 0.9942 - val_loss: 0.0421 - val_accuracy: 0.9922\n",
      "Epoch 62/100\n",
      "38400/38400 [==============================] - 6s 145us/step - loss: 0.0226 - accuracy: 0.9942 - val_loss: 0.0414 - val_accuracy: 0.9923\n",
      "Epoch 63/100\n",
      "38400/38400 [==============================] - 6s 149us/step - loss: 0.0215 - accuracy: 0.9943 - val_loss: 0.0420 - val_accuracy: 0.9915\n",
      "Epoch 64/100\n",
      "38400/38400 [==============================] - 5s 140us/step - loss: 0.0198 - accuracy: 0.9946 - val_loss: 0.0466 - val_accuracy: 0.9920\n",
      "Epoch 65/100\n",
      "38400/38400 [==============================] - 5s 137us/step - loss: 0.0198 - accuracy: 0.9941 - val_loss: 0.0467 - val_accuracy: 0.9914\n",
      "Epoch 66/100\n",
      "38400/38400 [==============================] - 5s 123us/step - loss: 0.0219 - accuracy: 0.9943 - val_loss: 0.0416 - val_accuracy: 0.9919- accu\n",
      "Epoch 67/100\n",
      "38400/38400 [==============================] - 5s 123us/step - loss: 0.0225 - accuracy: 0.9941 - val_loss: 0.0441 - val_accuracy: 0.9921\n",
      "Epoch 68/100\n",
      "38400/38400 [==============================] - 6s 148us/step - loss: 0.0210 - accuracy: 0.9943 - val_loss: 0.0386 - val_accuracy: 0.9915\n",
      "Epoch 69/100\n",
      "38400/38400 [==============================] - 5s 138us/step - loss: 0.0217 - accuracy: 0.9940 - val_loss: 0.0393 - val_accuracy: 0.9916\n",
      "Epoch 70/100\n",
      "38400/38400 [==============================] - 5s 132us/step - loss: 0.0201 - accuracy: 0.9949 - val_loss: 0.0557 - val_accuracy: 0.9920\n",
      "Epoch 71/100\n",
      "38400/38400 [==============================] - 5s 136us/step - loss: 0.0206 - accuracy: 0.9943 - val_loss: 0.0548 - val_accuracy: 0.9916\n",
      "Epoch 72/100\n",
      "38400/38400 [==============================] - 6s 159us/step - loss: 0.0213 - accuracy: 0.9943 - val_loss: 0.0434 - val_accuracy: 0.9914\n",
      "Epoch 73/100\n",
      "38400/38400 [==============================] - 6s 151us/step - loss: 0.0224 - accuracy: 0.9944 - val_loss: 0.0431 - val_accuracy: 0.9912\n",
      "Epoch 74/100\n",
      "38400/38400 [==============================] - 5s 143us/step - loss: 0.0190 - accuracy: 0.9947 - val_loss: 0.0562 - val_accuracy: 0.9923\n",
      "Epoch 75/100\n",
      "38400/38400 [==============================] - 5s 142us/step - loss: 0.0196 - accuracy: 0.9942 - val_loss: 0.0665 - val_accuracy: 0.9917\n",
      "Epoch 76/100\n",
      "38400/38400 [==============================] - 6s 143us/step - loss: 0.0206 - accuracy: 0.9943 - val_loss: 0.0529 - val_accuracy: 0.9914\n",
      "Epoch 77/100\n",
      "38400/38400 [==============================] - 5s 136us/step - loss: 0.0202 - accuracy: 0.9944 - val_loss: 0.0510 - val_accuracy: 0.9920\n",
      "Epoch 78/100\n",
      "38400/38400 [==============================] - 5s 136us/step - loss: 0.0193 - accuracy: 0.9948 - val_loss: 0.0401 - val_accuracy: 0.9914\n",
      "Epoch 79/100\n",
      "38400/38400 [==============================] - 5s 134us/step - loss: 0.0197 - accuracy: 0.9943 - val_loss: 0.0468 - val_accuracy: 0.9919\n",
      "Epoch 80/100\n",
      "38400/38400 [==============================] - 6s 149us/step - loss: 0.0230 - accuracy: 0.9940 - val_loss: 0.0523 - val_accuracy: 0.9910\n",
      "Epoch 81/100\n",
      "38400/38400 [==============================] - 5s 140us/step - loss: 0.0205 - accuracy: 0.9946 - val_loss: 0.0422 - val_accuracy: 0.9916\n",
      "Epoch 82/100\n",
      "38400/38400 [==============================] - 5s 140us/step - loss: 0.0217 - accuracy: 0.9947 - val_loss: 0.0441 - val_accuracy: 0.9917\n",
      "Epoch 83/100\n",
      "38400/38400 [==============================] - 5s 141us/step - loss: 0.0192 - accuracy: 0.9945 - val_loss: 0.0519 - val_accuracy: 0.9919\n",
      "Epoch 84/100\n",
      "38400/38400 [==============================] - 5s 135us/step - loss: 0.0194 - accuracy: 0.9947 - val_loss: 0.0536 - val_accuracy: 0.9916\n",
      "Epoch 85/100\n",
      "38400/38400 [==============================] - 5s 133us/step - loss: 0.0207 - accuracy: 0.9945 - val_loss: 0.0462 - val_accuracy: 0.9912\n",
      "Epoch 86/100\n",
      "38400/38400 [==============================] - 5s 138us/step - loss: 0.0193 - accuracy: 0.9949 - val_loss: 0.0492 - val_accuracy: 0.9916\n",
      "Epoch 87/100\n",
      "38400/38400 [==============================] - 5s 134us/step - loss: 0.0199 - accuracy: 0.9945 - val_loss: 0.0627 - val_accuracy: 0.9919\n",
      "Epoch 88/100\n",
      "38400/38400 [==============================] - 5s 133us/step - loss: 0.0205 - accuracy: 0.9946 - val_loss: 0.0465 - val_accuracy: 0.9915\n",
      "Epoch 89/100\n",
      "38400/38400 [==============================] - 5s 136us/step - loss: 0.0190 - accuracy: 0.9948 - val_loss: 0.0477 - val_accuracy: 0.9920\n",
      "Epoch 90/100\n",
      "38400/38400 [==============================] - 6s 151us/step - loss: 0.0199 - accuracy: 0.9947 - val_loss: 0.0572 - val_accuracy: 0.9923\n",
      "Epoch 91/100\n",
      "38400/38400 [==============================] - 6s 163us/step - loss: 0.0194 - accuracy: 0.9949 - val_loss: 0.0558 - val_accuracy: 0.9911\n",
      "Epoch 92/100\n",
      "38400/38400 [==============================] - 6s 145us/step - loss: 0.0208 - accuracy: 0.9945 - val_loss: 0.0598 - val_accuracy: 0.9919\n",
      "Epoch 93/100\n",
      "38400/38400 [==============================] - 5s 139us/step - loss: 0.0182 - accuracy: 0.9952 - val_loss: 0.0438 - val_accuracy: 0.9912\n",
      "Epoch 94/100\n",
      "38400/38400 [==============================] - 6s 152us/step - loss: 0.0192 - accuracy: 0.9946 - val_loss: 0.0519 - val_accuracy: 0.9914\n",
      "Epoch 95/100\n",
      "38400/38400 [==============================] - 6s 143us/step - loss: 0.0201 - accuracy: 0.9946 - val_loss: 0.0520 - val_accuracy: 0.9906\n",
      "Epoch 96/100\n",
      "38400/38400 [==============================] - 6s 145us/step - loss: 0.0177 - accuracy: 0.9953 - val_loss: 0.0647 - val_accuracy: 0.9920\n",
      "Epoch 97/100\n",
      "38400/38400 [==============================] - 6s 155us/step - loss: 0.0187 - accuracy: 0.9951 - val_loss: 0.0605 - val_accuracy: 0.9914\n",
      "Epoch 98/100\n",
      "38400/38400 [==============================] - 6s 160us/step - loss: 0.0190 - accuracy: 0.9952 - val_loss: 0.0551 - val_accuracy: 0.9914\n",
      "Epoch 99/100\n",
      "38400/38400 [==============================] - 6s 157us/step - loss: 0.0199 - accuracy: 0.9951 - val_loss: 0.0556 - val_accuracy: 0.9915\n",
      "Epoch 100/100\n",
      "38400/38400 [==============================] - 6s 164us/step - loss: 0.0188 - accuracy: 0.9950 - val_loss: 0.0560 - val_accuracy: 0.9920\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x155bf23c8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the Model\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=1, validation_split=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12000/12000 [==============================] - 0s 30us/step\n",
      "The loss on the test set is 0.1148 and the accuracy is 98.9500%\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
    "print(f'The loss on the test set is {test_loss:.4f} and the accuracy is {test_acc*100:.4f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the null accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11788\n",
       "1      212\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the value_count function to calculate distinct class values\n",
    "y_test['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9823333333333333"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the null accuracy\n",
    "y_test['class'].value_counts(normalize=True).loc[0]"
   ]
  }
 ],
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