TF_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/navarra/snap/jupyter/common/lib/python3.7/site-packages/joblib/_multiprocessing_helpers.py:45: UserWarning: [Errno 13] Permission denied.  joblib will operate in serial mode\n",
      "  warnings.warn('%s.  joblib will operate in serial mode' % (e,))\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data and First Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('cancer_classification.csv')\n",
    "pd.set_option('display.max_columns', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>radius error</th>\n",
       "      <th>texture error</th>\n",
       "      <th>perimeter error</th>\n",
       "      <th>area error</th>\n",
       "      <th>smoothness error</th>\n",
       "      <th>compactness error</th>\n",
       "      <th>concavity error</th>\n",
       "      <th>concave points error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>fractal dimension error</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>benign_0__mal_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>1.0950</td>\n",
       "      <td>0.9053</td>\n",
       "      <td>8.589</td>\n",
       "      <td>153.40</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.04904</td>\n",
       "      <td>0.05373</td>\n",
       "      <td>0.01587</td>\n",
       "      <td>0.03003</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>0.5435</td>\n",
       "      <td>0.7339</td>\n",
       "      <td>3.398</td>\n",
       "      <td>74.08</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.01308</td>\n",
       "      <td>0.01860</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.01389</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>0.7456</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>4.585</td>\n",
       "      <td>94.03</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.04006</td>\n",
       "      <td>0.03832</td>\n",
       "      <td>0.02058</td>\n",
       "      <td>0.02250</td>\n",
       "      <td>0.004571</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>0.4956</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>3.445</td>\n",
       "      <td>27.23</td>\n",
       "      <td>0.009110</td>\n",
       "      <td>0.07458</td>\n",
       "      <td>0.05661</td>\n",
       "      <td>0.01867</td>\n",
       "      <td>0.05963</td>\n",
       "      <td>0.009208</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>0.7572</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>5.438</td>\n",
       "      <td>94.44</td>\n",
       "      <td>0.011490</td>\n",
       "      <td>0.02461</td>\n",
       "      <td>0.05688</td>\n",
       "      <td>0.01885</td>\n",
       "      <td>0.01756</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  radius error  texture error  perimeter error  \\\n",
       "0                 0.07871        1.0950         0.9053            8.589   \n",
       "1                 0.05667        0.5435         0.7339            3.398   \n",
       "2                 0.05999        0.7456         0.7869            4.585   \n",
       "3                 0.09744        0.4956         1.1560            3.445   \n",
       "4                 0.05883        0.7572         0.7813            5.438   \n",
       "\n",
       "   area error  smoothness error  compactness error  concavity error  \\\n",
       "0      153.40          0.006399            0.04904          0.05373   \n",
       "1       74.08          0.005225            0.01308          0.01860   \n",
       "2       94.03          0.006150            0.04006          0.03832   \n",
       "3       27.23          0.009110            0.07458          0.05661   \n",
       "4       94.44          0.011490            0.02461          0.05688   \n",
       "\n",
       "   concave points error  symmetry error  fractal dimension error  \\\n",
       "0               0.01587         0.03003                 0.006193   \n",
       "1               0.01340         0.01389                 0.003532   \n",
       "2               0.02058         0.02250                 0.004571   \n",
       "3               0.01867         0.05963                 0.009208   \n",
       "4               0.01885         0.01756                 0.005115   \n",
       "\n",
       "   worst radius  worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "0         25.38          17.33           184.60      2019.0            0.1622   \n",
       "1         24.99          23.41           158.80      1956.0            0.1238   \n",
       "2         23.57          25.53           152.50      1709.0            0.1444   \n",
       "3         14.91          26.50            98.87       567.7            0.2098   \n",
       "4         22.54          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   worst fractal dimension  benign_0__mal_1  \n",
       "0                  0.11890                0  \n",
       "1                  0.08902                0  \n",
       "2                  0.08758                0  \n",
       "3                  0.17300                0  \n",
       "4                  0.07678                0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>radius error</th>\n",
       "      <th>texture error</th>\n",
       "      <th>perimeter error</th>\n",
       "      <th>area error</th>\n",
       "      <th>smoothness error</th>\n",
       "      <th>compactness error</th>\n",
       "      <th>concavity error</th>\n",
       "      <th>concave points error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>fractal dimension error</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>benign_0__mal_1</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>0.405172</td>\n",
       "      <td>1.216853</td>\n",
       "      <td>2.866059</td>\n",
       "      <td>40.337079</td>\n",
       "      <td>0.007041</td>\n",
       "      <td>0.025478</td>\n",
       "      <td>0.031894</td>\n",
       "      <td>0.011796</td>\n",
       "      <td>0.020542</td>\n",
       "      <td>0.003795</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>0.627417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>0.277313</td>\n",
       "      <td>0.551648</td>\n",
       "      <td>2.021855</td>\n",
       "      <td>45.491006</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>0.017908</td>\n",
       "      <td>0.030186</td>\n",
       "      <td>0.006170</td>\n",
       "      <td>0.008266</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>0.483918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>0.111500</td>\n",
       "      <td>0.360200</td>\n",
       "      <td>0.757000</td>\n",
       "      <td>6.802000</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007882</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>0.232400</td>\n",
       "      <td>0.833900</td>\n",
       "      <td>1.606000</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>0.005169</td>\n",
       "      <td>0.013080</td>\n",
       "      <td>0.015090</td>\n",
       "      <td>0.007638</td>\n",
       "      <td>0.015160</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.324200</td>\n",
       "      <td>1.108000</td>\n",
       "      <td>2.287000</td>\n",
       "      <td>24.530000</td>\n",
       "      <td>0.006380</td>\n",
       "      <td>0.020450</td>\n",
       "      <td>0.025890</td>\n",
       "      <td>0.010930</td>\n",
       "      <td>0.018730</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>0.478900</td>\n",
       "      <td>1.474000</td>\n",
       "      <td>3.357000</td>\n",
       "      <td>45.190000</td>\n",
       "      <td>0.008146</td>\n",
       "      <td>0.032450</td>\n",
       "      <td>0.042050</td>\n",
       "      <td>0.014710</td>\n",
       "      <td>0.023480</td>\n",
       "      <td>0.004558</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>2.873000</td>\n",
       "      <td>4.885000</td>\n",
       "      <td>21.980000</td>\n",
       "      <td>542.200000</td>\n",
       "      <td>0.031130</td>\n",
       "      <td>0.135400</td>\n",
       "      <td>0.396000</td>\n",
       "      <td>0.052790</td>\n",
       "      <td>0.078950</td>\n",
       "      <td>0.029840</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean radius  mean texture  mean perimeter    mean area  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       mean symmetry  mean fractal dimension  radius error  texture error  \\\n",
       "count     569.000000              569.000000    569.000000     569.000000   \n",
       "mean        0.181162                0.062798      0.405172       1.216853   \n",
       "std         0.027414                0.007060      0.277313       0.551648   \n",
       "min         0.106000                0.049960      0.111500       0.360200   \n",
       "25%         0.161900                0.057700      0.232400       0.833900   \n",
       "50%         0.179200                0.061540      0.324200       1.108000   \n",
       "75%         0.195700                0.066120      0.478900       1.474000   \n",
       "max         0.304000                0.097440      2.873000       4.885000   \n",
       "\n",
       "       perimeter error  area error  smoothness error  compactness error  \\\n",
       "count       569.000000  569.000000        569.000000         569.000000   \n",
       "mean          2.866059   40.337079          0.007041           0.025478   \n",
       "std           2.021855   45.491006          0.003003           0.017908   \n",
       "min           0.757000    6.802000          0.001713           0.002252   \n",
       "25%           1.606000   17.850000          0.005169           0.013080   \n",
       "50%           2.287000   24.530000          0.006380           0.020450   \n",
       "75%           3.357000   45.190000          0.008146           0.032450   \n",
       "max          21.980000  542.200000          0.031130           0.135400   \n",
       "\n",
       "       concavity error  concave points error  symmetry error  \\\n",
       "count       569.000000            569.000000      569.000000   \n",
       "mean          0.031894              0.011796        0.020542   \n",
       "std           0.030186              0.006170        0.008266   \n",
       "min           0.000000              0.000000        0.007882   \n",
       "25%           0.015090              0.007638        0.015160   \n",
       "50%           0.025890              0.010930        0.018730   \n",
       "75%           0.042050              0.014710        0.023480   \n",
       "max           0.396000              0.052790        0.078950   \n",
       "\n",
       "       fractal dimension error  worst radius  worst texture  worst perimeter  \\\n",
       "count               569.000000    569.000000     569.000000       569.000000   \n",
       "mean                  0.003795     16.269190      25.677223       107.261213   \n",
       "std                   0.002646      4.833242       6.146258        33.602542   \n",
       "min                   0.000895      7.930000      12.020000        50.410000   \n",
       "25%                   0.002248     13.010000      21.080000        84.110000   \n",
       "50%                   0.003187     14.970000      25.410000        97.660000   \n",
       "75%                   0.004558     18.790000      29.720000       125.400000   \n",
       "max                   0.029840     36.040000      49.540000       251.200000   \n",
       "\n",
       "        worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "count   569.000000        569.000000         569.000000       569.000000   \n",
       "mean    880.583128          0.132369           0.254265         0.272188   \n",
       "std     569.356993          0.022832           0.157336         0.208624   \n",
       "min     185.200000          0.071170           0.027290         0.000000   \n",
       "25%     515.300000          0.116600           0.147200         0.114500   \n",
       "50%     686.500000          0.131300           0.211900         0.226700   \n",
       "75%    1084.000000          0.146000           0.339100         0.382900   \n",
       "max    4254.000000          0.222600           1.058000         1.252000   \n",
       "\n",
       "       worst concave points  worst symmetry  worst fractal dimension  \\\n",
       "count            569.000000      569.000000               569.000000   \n",
       "mean               0.114606        0.290076                 0.083946   \n",
       "std                0.065732        0.061867                 0.018061   \n",
       "min                0.000000        0.156500                 0.055040   \n",
       "25%                0.064930        0.250400                 0.071460   \n",
       "50%                0.099930        0.282200                 0.080040   \n",
       "75%                0.161400        0.317900                 0.092080   \n",
       "max                0.291000        0.663800                 0.207500   \n",
       "\n",
       "       benign_0__mal_1  \n",
       "count       569.000000  \n",
       "mean          0.627417  \n",
       "std           0.483918  \n",
       "min           0.000000  \n",
       "25%           0.000000  \n",
       "50%           1.000000  \n",
       "75%           1.000000  \n",
       "max           1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('benign_0__mal_1', data = df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "df.corr()['benign_0__mal_1'][:-1].sort_values(ascending = False).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split and Scale Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('benign_0__mal_1', axis = 1).values\n",
    "y = df['benign_0__mal_1'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426, 30)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.317e+01, 1.822e+01, 8.428e+01, ..., 1.045e-01, 2.235e-01,\n",
       "        6.925e-02],\n",
       "       [1.262e+01, 1.715e+01, 8.062e+01, ..., 9.851e-02, 3.270e-01,\n",
       "        7.330e-02],\n",
       "       [1.480e+01, 1.766e+01, 9.588e+01, ..., 8.308e-02, 3.600e-01,\n",
       "        7.285e-02],\n",
       "       ...,\n",
       "       [1.607e+01, 1.965e+01, 1.041e+02, ..., 1.520e-01, 2.650e-01,\n",
       "        6.387e-02],\n",
       "       [1.877e+01, 2.143e+01, 1.229e+02, ..., 2.048e-01, 3.679e-01,\n",
       "        9.870e-02],\n",
       "       [1.371e+01, 1.868e+01, 8.873e+01, ..., 1.284e-01, 2.849e-01,\n",
       "        9.031e-02]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.30280346, 0.28779168, 0.28292922, ..., 0.35910653, 0.13207175,\n",
       "        0.09320478],\n",
       "       [0.27589412, 0.25160636, 0.25735448, ..., 0.33852234, 0.33609304,\n",
       "        0.11976912],\n",
       "       [0.38255296, 0.26885357, 0.36398575, ..., 0.28549828, 0.40114331,\n",
       "        0.11681753],\n",
       "       ...,\n",
       "       [0.44468907, 0.3361515 , 0.42142408, ..., 0.52233677, 0.21387739,\n",
       "        0.05791683],\n",
       "       [0.57678947, 0.39634765, 0.55279156, ..., 0.70378007, 0.41671595,\n",
       "        0.2863702 ],\n",
       "       [0.32922354, 0.30334799, 0.31402418, ..., 0.44123711, 0.25310467,\n",
       "        0.23133937]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426, 30)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(30, input_shape = (30, ), activation = 'relu'))\n",
    "model.add(Dense(15, activation = 'relu'))\n",
    "\n",
    "model.add(Dense(1, activation = 'sigmoid'))  # sigmoid because it is a classfication problem\n",
    "\n",
    "model.compile(optimizer = 'adam', loss = 'binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 30)                930       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 15)                465       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 16        \n",
      "=================================================================\n",
      "Total params: 1,411\n",
      "Trainable params: 1,411\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "14/14 [==============================] - 0s 11ms/step - loss: 0.6714 - val_loss: 0.6355\n",
      "Epoch 2/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.6223 - val_loss: 0.5915\n",
      "Epoch 3/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.5768 - val_loss: 0.5439\n",
      "Epoch 4/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.5330 - val_loss: 0.5010\n",
      "Epoch 5/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.4851 - val_loss: 0.4497\n",
      "Epoch 6/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.4385 - val_loss: 0.4041\n",
      "Epoch 7/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3966 - val_loss: 0.3580\n",
      "Epoch 8/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3538 - val_loss: 0.3198\n",
      "Epoch 9/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3172 - val_loss: 0.2845\n",
      "Epoch 10/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2902 - val_loss: 0.2590\n",
      "Epoch 11/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2622 - val_loss: 0.2364\n",
      "Epoch 12/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2413 - val_loss: 0.2195\n",
      "Epoch 13/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2252 - val_loss: 0.2091\n",
      "Epoch 14/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.2095 - val_loss: 0.1930\n",
      "Epoch 15/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1970 - val_loss: 0.1827\n",
      "Epoch 16/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1868 - val_loss: 0.1768\n",
      "Epoch 17/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1773 - val_loss: 0.1693\n",
      "Epoch 18/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1685 - val_loss: 0.1594\n",
      "Epoch 19/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1588 - val_loss: 0.1565\n",
      "Epoch 20/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1507 - val_loss: 0.1489\n",
      "Epoch 21/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1447 - val_loss: 0.1457\n",
      "Epoch 22/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1366 - val_loss: 0.1445\n",
      "Epoch 23/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1296 - val_loss: 0.1380\n",
      "Epoch 24/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1242 - val_loss: 0.1347\n",
      "Epoch 25/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1197 - val_loss: 0.1323\n",
      "Epoch 26/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1169 - val_loss: 0.1345\n",
      "Epoch 27/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1099 - val_loss: 0.1296\n",
      "Epoch 28/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1049 - val_loss: 0.1271\n",
      "Epoch 29/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1014 - val_loss: 0.1309\n",
      "Epoch 30/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0991 - val_loss: 0.1291\n",
      "Epoch 31/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0947 - val_loss: 0.1232\n",
      "Epoch 32/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0955 - val_loss: 0.1322\n",
      "Epoch 33/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0891 - val_loss: 0.1220\n",
      "Epoch 34/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0885 - val_loss: 0.1232\n",
      "Epoch 35/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0836 - val_loss: 0.1213\n",
      "Epoch 36/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0822 - val_loss: 0.1256\n",
      "Epoch 37/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0814 - val_loss: 0.1188\n",
      "Epoch 38/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0787 - val_loss: 0.1232\n",
      "Epoch 39/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0779 - val_loss: 0.1235\n",
      "Epoch 40/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0735 - val_loss: 0.1187\n",
      "Epoch 41/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0725 - val_loss: 0.1252\n",
      "Epoch 42/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0733 - val_loss: 0.1193\n",
      "Epoch 43/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0711 - val_loss: 0.1223\n",
      "Epoch 44/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0716 - val_loss: 0.1182\n",
      "Epoch 45/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0687 - val_loss: 0.1222\n",
      "Epoch 46/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0675 - val_loss: 0.1185\n",
      "Epoch 47/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0657 - val_loss: 0.1232\n",
      "Epoch 48/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0644 - val_loss: 0.1192\n",
      "Epoch 49/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0676 - val_loss: 0.1263\n",
      "Epoch 50/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0628 - val_loss: 0.1197\n",
      "Epoch 51/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0638 - val_loss: 0.1258\n",
      "Epoch 52/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0625 - val_loss: 0.1264\n",
      "Epoch 53/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0633 - val_loss: 0.1261\n",
      "Epoch 54/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0640 - val_loss: 0.1197\n",
      "Epoch 55/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0615 - val_loss: 0.1226\n",
      "Epoch 56/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0620 - val_loss: 0.1275\n",
      "Epoch 57/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0610 - val_loss: 0.1282\n",
      "Epoch 58/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0603 - val_loss: 0.1240\n",
      "Epoch 59/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0579 - val_loss: 0.1209\n",
      "Epoch 60/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0582 - val_loss: 0.1288\n",
      "Epoch 61/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0571 - val_loss: 0.1253\n",
      "Epoch 62/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0616 - val_loss: 0.1346\n",
      "Epoch 63/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0602 - val_loss: 0.1208\n",
      "Epoch 64/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0594 - val_loss: 0.1342\n",
      "Epoch 65/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0578 - val_loss: 0.1207\n",
      "Epoch 66/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0552 - val_loss: 0.1318\n",
      "Epoch 67/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0560 - val_loss: 0.1230\n",
      "Epoch 68/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0590 - val_loss: 0.1271\n",
      "Epoch 69/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0655 - val_loss: 0.1277\n",
      "Epoch 70/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0662 - val_loss: 0.1241\n",
      "Epoch 71/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0541 - val_loss: 0.1229\n",
      "Epoch 72/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0610 - val_loss: 0.1291\n",
      "Epoch 73/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0600 - val_loss: 0.1281\n",
      "Epoch 74/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0587 - val_loss: 0.1245\n",
      "Epoch 75/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0529 - val_loss: 0.1261\n",
      "Epoch 76/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0516 - val_loss: 0.1259\n",
      "Epoch 77/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0518 - val_loss: 0.1283\n",
      "Epoch 78/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0508 - val_loss: 0.1250\n",
      "Epoch 79/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0510 - val_loss: 0.1249\n",
      "Epoch 80/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0531 - val_loss: 0.1234\n",
      "Epoch 81/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0538 - val_loss: 0.1311\n",
      "Epoch 82/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0542 - val_loss: 0.1246\n",
      "Epoch 83/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0500 - val_loss: 0.1241\n",
      "Epoch 84/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0500 - val_loss: 0.1333\n",
      "Epoch 85/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0518 - val_loss: 0.1261\n",
      "Epoch 86/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0495 - val_loss: 0.1268\n",
      "Epoch 87/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0490 - val_loss: 0.1305\n",
      "Epoch 88/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0499 - val_loss: 0.1257\n",
      "Epoch 89/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0504 - val_loss: 0.1293\n",
      "Epoch 90/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0489 - val_loss: 0.1339\n",
      "Epoch 91/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0485 - val_loss: 0.1278\n",
      "Epoch 92/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0478 - val_loss: 0.1300\n",
      "Epoch 93/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0481 - val_loss: 0.1246\n",
      "Epoch 94/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0473 - val_loss: 0.1317\n",
      "Epoch 95/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0490 - val_loss: 0.1326\n",
      "Epoch 96/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0602 - val_loss: 0.1281\n",
      "Epoch 97/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0587 - val_loss: 0.1258\n",
      "Epoch 98/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0478 - val_loss: 0.1292\n",
      "Epoch 99/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0460 - val_loss: 0.1302\n",
      "Epoch 100/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0462 - val_loss: 0.1273\n",
      "Epoch 101/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0471 - val_loss: 0.1308\n",
      "Epoch 102/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0464 - val_loss: 0.1294\n",
      "Epoch 103/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0467 - val_loss: 0.1362\n",
      "Epoch 104/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0459 - val_loss: 0.1375\n",
      "Epoch 105/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0440 - val_loss: 0.1268\n",
      "Epoch 106/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0464 - val_loss: 0.1328\n",
      "Epoch 107/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0453 - val_loss: 0.1334\n",
      "Epoch 108/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0436 - val_loss: 0.1289\n",
      "Epoch 109/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0438 - val_loss: 0.1321\n",
      "Epoch 110/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0446 - val_loss: 0.1337\n",
      "Epoch 111/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0434 - val_loss: 0.1306\n",
      "Epoch 112/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0438 - val_loss: 0.1315\n",
      "Epoch 113/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0429 - val_loss: 0.1338\n",
      "Epoch 114/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0427 - val_loss: 0.1327\n",
      "Epoch 115/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0431 - val_loss: 0.1382\n",
      "Epoch 116/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0474 - val_loss: 0.1363\n",
      "Epoch 117/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0454 - val_loss: 0.1300\n",
      "Epoch 118/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0483 - val_loss: 0.1287\n",
      "Epoch 119/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0422 - val_loss: 0.1321\n",
      "Epoch 120/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0447 - val_loss: 0.1363\n",
      "Epoch 121/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0433 - val_loss: 0.1307\n",
      "Epoch 122/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0445 - val_loss: 0.1319\n",
      "Epoch 123/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0426 - val_loss: 0.1368\n",
      "Epoch 124/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0423 - val_loss: 0.1329\n",
      "Epoch 125/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0431 - val_loss: 0.1323\n",
      "Epoch 126/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0399 - val_loss: 0.1424\n",
      "Epoch 127/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0416 - val_loss: 0.1405\n",
      "Epoch 128/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0412 - val_loss: 0.1413\n",
      "Epoch 129/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0405 - val_loss: 0.1341\n",
      "Epoch 130/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0395 - val_loss: 0.1370\n",
      "Epoch 131/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0416 - val_loss: 0.1318\n",
      "Epoch 132/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0412 - val_loss: 0.1310\n",
      "Epoch 133/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0423 - val_loss: 0.1356\n",
      "Epoch 134/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0430 - val_loss: 0.1396\n",
      "Epoch 135/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0436 - val_loss: 0.1405\n",
      "Epoch 136/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0397 - val_loss: 0.1335\n",
      "Epoch 137/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0387 - val_loss: 0.1387\n",
      "Epoch 138/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0400 - val_loss: 0.1424\n",
      "Epoch 139/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0405 - val_loss: 0.1405\n",
      "Epoch 140/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0405 - val_loss: 0.1393\n",
      "Epoch 141/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0392 - val_loss: 0.1372\n",
      "Epoch 142/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0375 - val_loss: 0.1370\n",
      "Epoch 143/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0373 - val_loss: 0.1415\n",
      "Epoch 144/600\n",
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      "Epoch 145/600\n",
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    },
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     ]
    },
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     "text": [
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      "Epoch 482/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0070 - val_loss: 0.2318\n",
      "Epoch 483/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0071 - val_loss: 0.2395\n",
      "Epoch 484/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0065 - val_loss: 0.2228\n",
      "Epoch 485/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0062 - val_loss: 0.2412\n",
      "Epoch 486/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0064 - val_loss: 0.2269\n",
      "Epoch 487/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0064 - val_loss: 0.2503\n",
      "Epoch 488/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0065 - val_loss: 0.2364\n",
      "Epoch 489/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0071 - val_loss: 0.2973\n",
      "Epoch 490/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0130 - val_loss: 0.2119\n",
      "Epoch 491/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0066 - val_loss: 0.2634\n",
      "Epoch 492/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0061 - val_loss: 0.2314\n",
      "Epoch 493/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0071 - val_loss: 0.2233\n",
      "Epoch 494/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0075 - val_loss: 0.2647\n",
      "Epoch 495/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0066 - val_loss: 0.2313\n",
      "Epoch 496/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0058 - val_loss: 0.2387\n",
      "Epoch 497/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0059 - val_loss: 0.2284\n",
      "Epoch 498/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0063 - val_loss: 0.2345\n",
      "Epoch 499/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0058 - val_loss: 0.2403\n",
      "Epoch 500/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0059 - val_loss: 0.2379\n",
      "Epoch 501/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0059 - val_loss: 0.2316\n",
      "Epoch 502/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0069 - val_loss: 0.2478\n",
      "Epoch 503/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0060 - val_loss: 0.2494\n",
      "Epoch 504/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0058 - val_loss: 0.2292\n",
      "Epoch 505/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0057 - val_loss: 0.2681\n",
      "Epoch 506/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0057 - val_loss: 0.2380\n",
      "Epoch 507/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0053 - val_loss: 0.2428\n",
      "Epoch 508/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0057 - val_loss: 0.2361\n",
      "Epoch 509/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0052 - val_loss: 0.2630\n",
      "Epoch 510/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0056 - val_loss: 0.2428\n",
      "Epoch 511/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0053 - val_loss: 0.2534\n",
      "Epoch 512/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0052 - val_loss: 0.2478\n",
      "Epoch 513/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0051 - val_loss: 0.2533\n",
      "Epoch 514/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0055 - val_loss: 0.2534\n",
      "Epoch 515/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0055 - val_loss: 0.2568\n",
      "Epoch 516/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0050 - val_loss: 0.2570\n",
      "Epoch 517/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0051 - val_loss: 0.2463\n",
      "Epoch 518/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0048 - val_loss: 0.2565\n",
      "Epoch 519/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0052 - val_loss: 0.2685\n",
      "Epoch 520/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0050 - val_loss: 0.2385\n",
      "Epoch 521/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0056 - val_loss: 0.2630\n",
      "Epoch 522/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0050 - val_loss: 0.2631\n",
      "Epoch 523/600\n",
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      "Epoch 524/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0060 - val_loss: 0.2543\n",
      "Epoch 525/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0054 - val_loss: 0.2745\n",
      "Epoch 526/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0050 - val_loss: 0.2660\n",
      "Epoch 527/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0046 - val_loss: 0.2391\n",
      "Epoch 528/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0079 - val_loss: 0.2561\n",
      "Epoch 529/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0107 - val_loss: 0.2651\n",
      "Epoch 530/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0047 - val_loss: 0.2484\n",
      "Epoch 531/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0051 - val_loss: 0.2677\n",
      "Epoch 532/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0048 - val_loss: 0.2552\n",
      "Epoch 533/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0045 - val_loss: 0.2724\n",
      "Epoch 534/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0052 - val_loss: 0.2805\n",
      "Epoch 535/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0053 - val_loss: 0.2682\n",
      "Epoch 536/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0049 - val_loss: 0.2519\n",
      "Epoch 537/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0053 - val_loss: 0.3005\n",
      "Epoch 538/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0050 - val_loss: 0.2481\n",
      "Epoch 539/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0045 - val_loss: 0.2637\n",
      "Epoch 540/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0046 - val_loss: 0.2920\n",
      "Epoch 541/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0047 - val_loss: 0.2502\n",
      "Epoch 542/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0071 - val_loss: 0.2925\n",
      "Epoch 543/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0054 - val_loss: 0.2552\n",
      "Epoch 544/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0054 - val_loss: 0.2556\n",
      "Epoch 545/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0050 - val_loss: 0.2712\n",
      "Epoch 546/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0043 - val_loss: 0.2876\n",
      "Epoch 547/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0042 - val_loss: 0.2631\n",
      "Epoch 548/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0040 - val_loss: 0.3040\n",
      "Epoch 549/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0072 - val_loss: 0.2472\n",
      "Epoch 550/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0119 - val_loss: 0.2392\n",
      "Epoch 551/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0087 - val_loss: 0.2659\n",
      "Epoch 552/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0067 - val_loss: 0.2977\n",
      "Epoch 553/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0073 - val_loss: 0.2489\n",
      "Epoch 554/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0046 - val_loss: 0.3157\n",
      "Epoch 555/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0047 - val_loss: 0.2653\n",
      "Epoch 556/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0042 - val_loss: 0.2748\n",
      "Epoch 557/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0043 - val_loss: 0.2481\n",
      "Epoch 558/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0087 - val_loss: 0.2748\n",
      "Epoch 559/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0046 - val_loss: 0.2888\n",
      "Epoch 560/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0039 - val_loss: 0.2863\n",
      "Epoch 561/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0035 - val_loss: 0.2729\n",
      "Epoch 562/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0041 - val_loss: 0.2820\n",
      "Epoch 563/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0051 - val_loss: 0.3613\n",
      "Epoch 564/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0068 - val_loss: 0.2814\n",
      "Epoch 565/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0139 - val_loss: 0.2409\n",
      "Epoch 566/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0076 - val_loss: 0.3264\n",
      "Epoch 567/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0073 - val_loss: 0.2508\n",
      "Epoch 568/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0064 - val_loss: 0.3226\n",
      "Epoch 569/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.2613\n",
      "Epoch 570/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0040 - val_loss: 0.2992\n",
      "Epoch 571/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0049 - val_loss: 0.2623\n",
      "Epoch 572/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0051 - val_loss: 0.3217\n",
      "Epoch 573/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.2799\n",
      "Epoch 574/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.2984\n",
      "Epoch 575/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.2959\n",
      "Epoch 576/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.2945\n",
      "Epoch 577/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.2778\n",
      "Epoch 578/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.3182\n",
      "Epoch 579/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0040 - val_loss: 0.2839\n",
      "Epoch 580/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0051 - val_loss: 0.3025\n",
      "Epoch 581/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.2933\n",
      "Epoch 582/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.3139\n",
      "Epoch 583/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.2819\n",
      "Epoch 584/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.3010\n",
      "Epoch 585/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.2967\n",
      "Epoch 586/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.3073\n",
      "Epoch 587/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0040 - val_loss: 0.3112\n",
      "Epoch 588/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.2976\n",
      "Epoch 589/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.3113\n",
      "Epoch 590/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.3043\n",
      "Epoch 591/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0039 - val_loss: 0.3164\n",
      "Epoch 592/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.2838\n",
      "Epoch 593/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.3226\n",
      "Epoch 594/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.2948\n",
      "Epoch 595/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.3145\n",
      "Epoch 596/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0039 - val_loss: 0.3371\n",
      "Epoch 597/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.3026\n",
      "Epoch 598/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.3087\n",
      "Epoch 599/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0052 - val_loss: 0.3469\n",
      "Epoch 600/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0042 - val_loss: 0.2770\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f691c5fc358>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x = X_train, y = y_train, validation_data = (X_test, y_test), epochs = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is an overfitting so we are creating our model again with early stop to prevent overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(30, input_shape = (30, ), activation = 'relu'))\n",
    "model.add(Dense(15, activation = 'relu'))\n",
    "\n",
    "model.add(Dense(1, activation = 'sigmoid'))  # sigmoid because it is a classfication problem\n",
    "\n",
    "model.compile(optimizer = 'adam', loss = 'binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor = 'val_loss', mode = 'min', verbose = 1, patience = 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.7132 - val_loss: 0.6911\n",
      "Epoch 2/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.6810 - val_loss: 0.6641\n",
      "Epoch 3/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.6545 - val_loss: 0.6412\n",
      "Epoch 4/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.6285 - val_loss: 0.6111\n",
      "Epoch 5/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.5952 - val_loss: 0.5735\n",
      "Epoch 6/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.5568 - val_loss: 0.5326\n",
      "Epoch 7/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.5154 - val_loss: 0.4866\n",
      "Epoch 8/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.4693 - val_loss: 0.4374\n",
      "Epoch 9/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.4229 - val_loss: 0.3890\n",
      "Epoch 10/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3791 - val_loss: 0.3425\n",
      "Epoch 11/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3418 - val_loss: 0.3061\n",
      "Epoch 12/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3115 - val_loss: 0.2751\n",
      "Epoch 13/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2834 - val_loss: 0.2529\n",
      "Epoch 14/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2634 - val_loss: 0.2286\n",
      "Epoch 15/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2449 - val_loss: 0.2138\n",
      "Epoch 16/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2311 - val_loss: 0.1974\n",
      "Epoch 17/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2168 - val_loss: 0.1897\n",
      "Epoch 18/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.2079 - val_loss: 0.1743\n",
      "Epoch 19/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1944 - val_loss: 0.1731\n",
      "Epoch 20/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1863 - val_loss: 0.1586\n",
      "Epoch 21/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1765 - val_loss: 0.1529\n",
      "Epoch 22/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1712 - val_loss: 0.1443\n",
      "Epoch 23/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1632 - val_loss: 0.1396\n",
      "Epoch 24/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1554 - val_loss: 0.1347\n",
      "Epoch 25/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1499 - val_loss: 0.1350\n",
      "Epoch 26/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1466 - val_loss: 0.1275\n",
      "Epoch 27/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1417 - val_loss: 0.1253\n",
      "Epoch 28/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1325 - val_loss: 0.1207\n",
      "Epoch 29/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1272 - val_loss: 0.1167\n",
      "Epoch 30/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1229 - val_loss: 0.1123\n",
      "Epoch 31/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1162 - val_loss: 0.1108\n",
      "Epoch 32/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1145 - val_loss: 0.1085\n",
      "Epoch 33/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1092 - val_loss: 0.1065\n",
      "Epoch 34/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1065 - val_loss: 0.1027\n",
      "Epoch 35/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1022 - val_loss: 0.1046\n",
      "Epoch 36/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0982 - val_loss: 0.0980\n",
      "Epoch 37/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0934 - val_loss: 0.0982\n",
      "Epoch 38/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0913 - val_loss: 0.0976\n",
      "Epoch 39/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0897 - val_loss: 0.0977\n",
      "Epoch 40/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0875 - val_loss: 0.0953\n",
      "Epoch 41/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0903 - val_loss: 0.0956\n",
      "Epoch 42/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0958 - val_loss: 0.0910\n",
      "Epoch 43/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0852 - val_loss: 0.0914\n",
      "Epoch 44/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0794 - val_loss: 0.0928\n",
      "Epoch 45/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0785 - val_loss: 0.0901\n",
      "Epoch 46/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0765 - val_loss: 0.0917\n",
      "Epoch 47/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0753 - val_loss: 0.0905\n",
      "Epoch 48/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0736 - val_loss: 0.0886\n",
      "Epoch 49/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0738 - val_loss: 0.0886\n",
      "Epoch 50/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0717 - val_loss: 0.0918\n",
      "Epoch 51/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0709 - val_loss: 0.0883\n",
      "Epoch 52/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0714 - val_loss: 0.0896\n",
      "Epoch 53/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0700 - val_loss: 0.0929\n",
      "Epoch 54/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0685 - val_loss: 0.0884\n",
      "Epoch 55/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0678 - val_loss: 0.0909\n",
      "Epoch 56/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0679 - val_loss: 0.0891\n",
      "Epoch 57/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0655 - val_loss: 0.0896\n",
      "Epoch 58/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0648 - val_loss: 0.0870\n",
      "Epoch 59/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0650 - val_loss: 0.0871\n",
      "Epoch 60/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0638 - val_loss: 0.0882\n",
      "Epoch 61/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0629 - val_loss: 0.0906\n",
      "Epoch 62/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0663 - val_loss: 0.0876\n",
      "Epoch 63/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0648 - val_loss: 0.0869\n",
      "Epoch 64/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0615 - val_loss: 0.0867\n",
      "Epoch 65/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0624 - val_loss: 0.0878\n",
      "Epoch 66/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0640 - val_loss: 0.0866\n",
      "Epoch 67/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0614 - val_loss: 0.0885\n",
      "Epoch 68/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0626 - val_loss: 0.0860\n",
      "Epoch 69/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0619 - val_loss: 0.0872\n",
      "Epoch 70/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0586 - val_loss: 0.0877\n",
      "Epoch 71/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0585 - val_loss: 0.0900\n",
      "Epoch 72/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0626 - val_loss: 0.0880\n",
      "Epoch 73/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0680 - val_loss: 0.0861\n",
      "Epoch 74/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0581 - val_loss: 0.0895\n",
      "Epoch 75/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0564 - val_loss: 0.0858\n",
      "Epoch 76/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0565 - val_loss: 0.0907\n",
      "Epoch 77/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0596 - val_loss: 0.0859\n",
      "Epoch 78/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0552 - val_loss: 0.0911\n",
      "Epoch 79/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0568 - val_loss: 0.0860\n",
      "Epoch 80/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0595 - val_loss: 0.0866\n",
      "Epoch 81/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0610 - val_loss: 0.0893\n",
      "Epoch 82/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0556 - val_loss: 0.0873\n",
      "Epoch 83/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0554 - val_loss: 0.0868\n",
      "Epoch 84/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0573 - val_loss: 0.0890\n",
      "Epoch 85/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0535 - val_loss: 0.0853\n",
      "Epoch 86/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0533 - val_loss: 0.0925\n",
      "Epoch 87/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0531 - val_loss: 0.0871\n",
      "Epoch 88/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0538 - val_loss: 0.0886\n",
      "Epoch 89/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0528 - val_loss: 0.0934\n",
      "Epoch 90/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0528 - val_loss: 0.0971\n",
      "Epoch 91/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0531 - val_loss: 0.0877\n",
      "Epoch 92/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0524 - val_loss: 0.0893\n",
      "Epoch 93/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0514 - val_loss: 0.0927\n",
      "Epoch 94/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0519 - val_loss: 0.0895\n",
      "Epoch 95/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0517 - val_loss: 0.0935\n",
      "Epoch 96/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0515 - val_loss: 0.0913\n",
      "Epoch 97/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0511 - val_loss: 0.0895\n",
      "Epoch 98/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0519 - val_loss: 0.0901\n",
      "Epoch 99/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0535 - val_loss: 0.0946\n",
      "Epoch 100/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0555 - val_loss: 0.0908\n",
      "Epoch 101/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0528 - val_loss: 0.0907\n",
      "Epoch 102/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0496 - val_loss: 0.0931\n",
      "Epoch 103/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0509 - val_loss: 0.0951\n",
      "Epoch 104/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0493 - val_loss: 0.0905\n",
      "Epoch 105/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0513 - val_loss: 0.0906\n",
      "Epoch 106/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0504 - val_loss: 0.0935\n",
      "Epoch 107/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0498 - val_loss: 0.0951\n",
      "Epoch 108/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0484 - val_loss: 0.0971\n",
      "Epoch 109/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0499 - val_loss: 0.0903\n",
      "Epoch 110/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0525 - val_loss: 0.0991\n",
      "Epoch 00110: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f690c62cf60>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x = X_train, y = y_train, validation_data = (X_test, y_test), epochs = 600,\n",
    "         callbacks = [early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We are creating our model again. This time we will use Dropout to prevent overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(30, input_shape = (30, ), activation = 'relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(15, activation = 'relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(1, activation = 'sigmoid'))  # sigmoid because it is a classfication problem\n",
    "\n",
    "model.compile(optimizer = 'adam', loss = 'binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.7039 - val_loss: 0.6751\n",
      "Epoch 2/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.6681 - val_loss: 0.6633\n",
      "Epoch 3/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.6603 - val_loss: 0.6453\n",
      "Epoch 4/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.6514 - val_loss: 0.6201\n",
      "Epoch 5/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.6226 - val_loss: 0.6006\n",
      "Epoch 6/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.6243 - val_loss: 0.5777\n",
      "Epoch 7/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.5993 - val_loss: 0.5552\n",
      "Epoch 8/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.5769 - val_loss: 0.5273\n",
      "Epoch 9/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.5636 - val_loss: 0.5024\n",
      "Epoch 10/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.5438 - val_loss: 0.4769\n",
      "Epoch 11/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.5213 - val_loss: 0.4522\n",
      "Epoch 12/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.5065 - val_loss: 0.4286\n",
      "Epoch 13/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.4873 - val_loss: 0.4005\n",
      "Epoch 14/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.4757 - val_loss: 0.3804\n",
      "Epoch 15/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.4377 - val_loss: 0.3543\n",
      "Epoch 16/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.4320 - val_loss: 0.3339\n",
      "Epoch 17/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.4298 - val_loss: 0.3139\n",
      "Epoch 18/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3867 - val_loss: 0.3012\n",
      "Epoch 19/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.3874 - val_loss: 0.2797\n",
      "Epoch 20/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.3457 - val_loss: 0.2681\n",
      "Epoch 21/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3466 - val_loss: 0.2469\n",
      "Epoch 22/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3263 - val_loss: 0.2339\n",
      "Epoch 23/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3314 - val_loss: 0.2218\n",
      "Epoch 24/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3192 - val_loss: 0.2117\n",
      "Epoch 25/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.3112 - val_loss: 0.2091\n",
      "Epoch 26/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.3177 - val_loss: 0.1993\n",
      "Epoch 27/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3242 - val_loss: 0.1911\n",
      "Epoch 28/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2790 - val_loss: 0.1893\n",
      "Epoch 29/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.2483 - val_loss: 0.1702\n",
      "Epoch 30/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2671 - val_loss: 0.1651\n",
      "Epoch 31/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2741 - val_loss: 0.1601\n",
      "Epoch 32/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2564 - val_loss: 0.1548\n",
      "Epoch 33/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2464 - val_loss: 0.1588\n",
      "Epoch 34/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2376 - val_loss: 0.1496\n",
      "Epoch 35/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2101 - val_loss: 0.1498\n",
      "Epoch 36/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2402 - val_loss: 0.1417\n",
      "Epoch 37/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2429 - val_loss: 0.1345\n",
      "Epoch 38/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2050 - val_loss: 0.1313\n",
      "Epoch 39/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2425 - val_loss: 0.1278\n",
      "Epoch 40/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2325 - val_loss: 0.1269\n",
      "Epoch 41/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2057 - val_loss: 0.1348\n",
      "Epoch 42/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2084 - val_loss: 0.1195\n",
      "Epoch 43/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2081 - val_loss: 0.1165\n",
      "Epoch 44/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1986 - val_loss: 0.1194\n",
      "Epoch 45/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1818 - val_loss: 0.1144\n",
      "Epoch 46/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1984 - val_loss: 0.1074\n",
      "Epoch 47/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2081 - val_loss: 0.1218\n",
      "Epoch 48/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.2093 - val_loss: 0.1056\n",
      "Epoch 49/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1972 - val_loss: 0.1073\n",
      "Epoch 50/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1601 - val_loss: 0.1026\n",
      "Epoch 51/600\n",
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      "Epoch 56/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1646 - val_loss: 0.0983\n",
      "Epoch 57/600\n",
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      "Epoch 58/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1456 - val_loss: 0.0942\n",
      "Epoch 59/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1532 - val_loss: 0.0941\n",
      "Epoch 60/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1740 - val_loss: 0.1005\n",
      "Epoch 61/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1330 - val_loss: 0.0914\n",
      "Epoch 62/600\n",
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      "Epoch 63/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1482 - val_loss: 0.0886\n",
      "Epoch 64/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1461 - val_loss: 0.0972\n",
      "Epoch 65/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1413 - val_loss: 0.0878\n",
      "Epoch 66/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1324 - val_loss: 0.0900\n",
      "Epoch 67/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1288 - val_loss: 0.0874\n",
      "Epoch 68/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1357 - val_loss: 0.0838\n",
      "Epoch 69/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1371 - val_loss: 0.0826\n",
      "Epoch 70/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1368 - val_loss: 0.0830\n",
      "Epoch 71/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1383 - val_loss: 0.0861\n",
      "Epoch 72/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1324 - val_loss: 0.1008\n",
      "Epoch 73/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1359 - val_loss: 0.0841\n",
      "Epoch 74/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1037 - val_loss: 0.0812\n",
      "Epoch 75/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0974 - val_loss: 0.1021\n",
      "Epoch 76/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1304 - val_loss: 0.0871\n",
      "Epoch 77/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1169 - val_loss: 0.0847\n",
      "Epoch 78/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1123 - val_loss: 0.0791\n",
      "Epoch 79/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1230 - val_loss: 0.0772\n",
      "Epoch 80/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1122 - val_loss: 0.0876\n",
      "Epoch 81/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1344 - val_loss: 0.0881\n",
      "Epoch 82/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1146 - val_loss: 0.0763\n",
      "Epoch 83/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1229 - val_loss: 0.0791\n",
      "Epoch 84/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1299 - val_loss: 0.0800\n",
      "Epoch 85/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1098 - val_loss: 0.0775\n",
      "Epoch 86/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1167 - val_loss: 0.0894\n",
      "Epoch 87/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0990 - val_loss: 0.0810\n",
      "Epoch 88/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1095 - val_loss: 0.0875\n",
      "Epoch 89/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1079 - val_loss: 0.0872\n",
      "Epoch 90/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1087 - val_loss: 0.0731\n",
      "Epoch 91/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1177 - val_loss: 0.0823\n",
      "Epoch 92/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0996 - val_loss: 0.0837\n",
      "Epoch 93/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1155 - val_loss: 0.0836\n",
      "Epoch 94/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0951 - val_loss: 0.0959\n",
      "Epoch 95/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1168 - val_loss: 0.0756\n",
      "Epoch 96/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1141 - val_loss: 0.0818\n",
      "Epoch 97/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0944 - val_loss: 0.0808\n",
      "Epoch 98/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1261 - val_loss: 0.0754\n",
      "Epoch 99/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1071 - val_loss: 0.0896\n",
      "Epoch 100/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1027 - val_loss: 0.0779\n",
      "Epoch 101/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0950 - val_loss: 0.0769\n",
      "Epoch 102/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1074 - val_loss: 0.0775\n",
      "Epoch 103/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1062 - val_loss: 0.0760\n",
      "Epoch 104/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0968 - val_loss: 0.0757\n",
      "Epoch 105/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0932 - val_loss: 0.0752\n",
      "Epoch 106/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0990 - val_loss: 0.0774\n",
      "Epoch 107/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0956 - val_loss: 0.1027\n",
      "Epoch 108/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1187 - val_loss: 0.0866\n",
      "Epoch 109/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1176 - val_loss: 0.0706\n",
      "Epoch 110/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1020 - val_loss: 0.0875\n",
      "Epoch 111/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1118 - val_loss: 0.0885\n",
      "Epoch 112/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0878 - val_loss: 0.0768\n",
      "Epoch 113/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1022 - val_loss: 0.0800\n",
      "Epoch 114/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0981 - val_loss: 0.0744\n",
      "Epoch 115/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1137 - val_loss: 0.0799\n",
      "Epoch 116/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0854 - val_loss: 0.0767\n",
      "Epoch 117/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0962 - val_loss: 0.0697\n",
      "Epoch 118/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1085 - val_loss: 0.0772\n",
      "Epoch 119/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1100 - val_loss: 0.0834\n",
      "Epoch 120/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0837 - val_loss: 0.0836\n",
      "Epoch 121/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1067 - val_loss: 0.0852\n",
      "Epoch 122/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0862 - val_loss: 0.0919\n",
      "Epoch 123/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0923 - val_loss: 0.0755\n",
      "Epoch 124/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0953 - val_loss: 0.0768\n",
      "Epoch 125/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0928 - val_loss: 0.0764\n",
      "Epoch 126/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0770 - val_loss: 0.0752\n",
      "Epoch 127/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0878 - val_loss: 0.0818\n",
      "Epoch 128/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0849 - val_loss: 0.0821\n",
      "Epoch 129/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0805 - val_loss: 0.0813\n",
      "Epoch 130/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.1023 - val_loss: 0.0796\n",
      "Epoch 131/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1053 - val_loss: 0.0697\n",
      "Epoch 132/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.1053 - val_loss: 0.0743\n",
      "Epoch 133/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1020 - val_loss: 0.0760\n",
      "Epoch 134/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0979 - val_loss: 0.0808\n",
      "Epoch 135/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0923 - val_loss: 0.0722\n",
      "Epoch 136/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0742 - val_loss: 0.0736\n",
      "Epoch 137/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0755 - val_loss: 0.0876\n",
      "Epoch 138/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0779 - val_loss: 0.0729\n",
      "Epoch 139/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0893 - val_loss: 0.0869\n",
      "Epoch 140/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0895 - val_loss: 0.0712\n",
      "Epoch 141/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0900 - val_loss: 0.0770\n",
      "Epoch 142/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1008 - val_loss: 0.0763\n",
      "Epoch 00142: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f690c4a3780>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x = X_train, y = y_train, validation_data = (X_test, y_test), epochs = 600,\n",
    "         callbacks = [early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-36-bc459dba29cd>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
      "Instructions for updating:\n",
      "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
     ]
    },
    {
     "data": {
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       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0]], dtype=int32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.98      0.97        55\n",
      "           1       0.99      0.98      0.98        88\n",
      "\n",
      "    accuracy                           0.98       143\n",
      "   macro avg       0.98      0.98      0.98       143\n",
      "weighted avg       0.98      0.98      0.98       143\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[54  1]\n",
      " [ 2 86]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 30)                930       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 30)                0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 15)                465       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 15)                0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 16        \n",
      "=================================================================\n",
      "Total params: 1,411\n",
      "Trainable params: 1,411\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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