Keras Cancer Detection Classification Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('/Users/namanmanchanda/Desktop/Keras_models/cancer_classification.csv')"
   ]
  },
  {
   "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>...</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>...</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>...</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>...</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>...</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>...</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",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "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  ...  worst texture  worst perimeter  worst area  \\\n",
       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
       "3                 0.09744  ...          26.50            98.87       567.7   \n",
       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  benign_0__mal_1  \n",
       "0          0.4601                  0.11890                0  \n",
       "1          0.2750                  0.08902                0  \n",
       "2          0.3613                  0.08758                0  \n",
       "3          0.6638                  0.17300                0  \n",
       "4          0.2364                  0.07678                0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.shape"
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   "cell_type": "code",
   "execution_count": 5,
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 31 columns):\n",
      "mean radius                569 non-null float64\n",
      "mean texture               569 non-null float64\n",
      "mean perimeter             569 non-null float64\n",
      "mean area                  569 non-null float64\n",
      "mean smoothness            569 non-null float64\n",
      "mean compactness           569 non-null float64\n",
      "mean concavity             569 non-null float64\n",
      "mean concave points        569 non-null float64\n",
      "mean symmetry              569 non-null float64\n",
      "mean fractal dimension     569 non-null float64\n",
      "radius error               569 non-null float64\n",
      "texture error              569 non-null float64\n",
      "perimeter error            569 non-null float64\n",
      "area error                 569 non-null float64\n",
      "smoothness error           569 non-null float64\n",
      "compactness error          569 non-null float64\n",
      "concavity error            569 non-null float64\n",
      "concave points error       569 non-null float64\n",
      "symmetry error             569 non-null float64\n",
      "fractal dimension error    569 non-null float64\n",
      "worst radius               569 non-null float64\n",
      "worst texture              569 non-null float64\n",
      "worst perimeter            569 non-null float64\n",
      "worst area                 569 non-null float64\n",
      "worst smoothness           569 non-null float64\n",
      "worst compactness          569 non-null float64\n",
      "worst concavity            569 non-null float64\n",
      "worst concave points       569 non-null float64\n",
      "worst symmetry             569 non-null float64\n",
      "worst fractal dimension    569 non-null float64\n",
      "benign_0__mal_1            569 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 137.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean radius</th>\n",
       "      <td>569.0</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>28.11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean texture</th>\n",
       "      <td>569.0</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>39.28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean perimeter</th>\n",
       "      <td>569.0</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>188.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean area</th>\n",
       "      <td>569.0</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>2501.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean smoothness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.16340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean compactness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.34540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean concavity</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.42680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean concave points</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.20120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean symmetry</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.30400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>0.09744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.405172</td>\n",
       "      <td>0.277313</td>\n",
       "      <td>0.111500</td>\n",
       "      <td>0.232400</td>\n",
       "      <td>0.324200</td>\n",
       "      <td>0.478900</td>\n",
       "      <td>2.87300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>1.216853</td>\n",
       "      <td>0.551648</td>\n",
       "      <td>0.360200</td>\n",
       "      <td>0.833900</td>\n",
       "      <td>1.108000</td>\n",
       "      <td>1.474000</td>\n",
       "      <td>4.88500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>2.866059</td>\n",
       "      <td>2.021855</td>\n",
       "      <td>0.757000</td>\n",
       "      <td>1.606000</td>\n",
       "      <td>2.287000</td>\n",
       "      <td>3.357000</td>\n",
       "      <td>21.98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>40.337079</td>\n",
       "      <td>45.491006</td>\n",
       "      <td>6.802000</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>24.530000</td>\n",
       "      <td>45.190000</td>\n",
       "      <td>542.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.007041</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>0.005169</td>\n",
       "      <td>0.006380</td>\n",
       "      <td>0.008146</td>\n",
       "      <td>0.03113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.025478</td>\n",
       "      <td>0.017908</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>0.013080</td>\n",
       "      <td>0.020450</td>\n",
       "      <td>0.032450</td>\n",
       "      <td>0.13540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.031894</td>\n",
       "      <td>0.030186</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015090</td>\n",
       "      <td>0.025890</td>\n",
       "      <td>0.042050</td>\n",
       "      <td>0.39600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.011796</td>\n",
       "      <td>0.006170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007638</td>\n",
       "      <td>0.010930</td>\n",
       "      <td>0.014710</td>\n",
       "      <td>0.05279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.020542</td>\n",
       "      <td>0.008266</td>\n",
       "      <td>0.007882</td>\n",
       "      <td>0.015160</td>\n",
       "      <td>0.018730</td>\n",
       "      <td>0.023480</td>\n",
       "      <td>0.07895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal dimension error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.003795</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>0.004558</td>\n",
       "      <td>0.02984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst radius</th>\n",
       "      <td>569.0</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>36.04000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst texture</th>\n",
       "      <td>569.0</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>49.54000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst perimeter</th>\n",
       "      <td>569.0</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>251.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst area</th>\n",
       "      <td>569.0</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>4254.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst smoothness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.22260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst compactness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>1.05800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst concavity</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>1.25200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst concave points</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.29100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst symmetry</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.66380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>0.20750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>benign_0__mal_1</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.627417</td>\n",
       "      <td>0.483918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count        mean         std         min  \\\n",
       "mean radius              569.0   14.127292    3.524049    6.981000   \n",
       "mean texture             569.0   19.289649    4.301036    9.710000   \n",
       "mean perimeter           569.0   91.969033   24.298981   43.790000   \n",
       "mean area                569.0  654.889104  351.914129  143.500000   \n",
       "mean smoothness          569.0    0.096360    0.014064    0.052630   \n",
       "mean compactness         569.0    0.104341    0.052813    0.019380   \n",
       "mean concavity           569.0    0.088799    0.079720    0.000000   \n",
       "mean concave points      569.0    0.048919    0.038803    0.000000   \n",
       "mean symmetry            569.0    0.181162    0.027414    0.106000   \n",
       "mean fractal dimension   569.0    0.062798    0.007060    0.049960   \n",
       "radius error             569.0    0.405172    0.277313    0.111500   \n",
       "texture error            569.0    1.216853    0.551648    0.360200   \n",
       "perimeter error          569.0    2.866059    2.021855    0.757000   \n",
       "area error               569.0   40.337079   45.491006    6.802000   \n",
       "smoothness error         569.0    0.007041    0.003003    0.001713   \n",
       "compactness error        569.0    0.025478    0.017908    0.002252   \n",
       "concavity error          569.0    0.031894    0.030186    0.000000   \n",
       "concave points error     569.0    0.011796    0.006170    0.000000   \n",
       "symmetry error           569.0    0.020542    0.008266    0.007882   \n",
       "fractal dimension error  569.0    0.003795    0.002646    0.000895   \n",
       "worst radius             569.0   16.269190    4.833242    7.930000   \n",
       "worst texture            569.0   25.677223    6.146258   12.020000   \n",
       "worst perimeter          569.0  107.261213   33.602542   50.410000   \n",
       "worst area               569.0  880.583128  569.356993  185.200000   \n",
       "worst smoothness         569.0    0.132369    0.022832    0.071170   \n",
       "worst compactness        569.0    0.254265    0.157336    0.027290   \n",
       "worst concavity          569.0    0.272188    0.208624    0.000000   \n",
       "worst concave points     569.0    0.114606    0.065732    0.000000   \n",
       "worst symmetry           569.0    0.290076    0.061867    0.156500   \n",
       "worst fractal dimension  569.0    0.083946    0.018061    0.055040   \n",
       "benign_0__mal_1          569.0    0.627417    0.483918    0.000000   \n",
       "\n",
       "                                25%         50%          75%         max  \n",
       "mean radius               11.700000   13.370000    15.780000    28.11000  \n",
       "mean texture              16.170000   18.840000    21.800000    39.28000  \n",
       "mean perimeter            75.170000   86.240000   104.100000   188.50000  \n",
       "mean area                420.300000  551.100000   782.700000  2501.00000  \n",
       "mean smoothness            0.086370    0.095870     0.105300     0.16340  \n",
       "mean compactness           0.064920    0.092630     0.130400     0.34540  \n",
       "mean concavity             0.029560    0.061540     0.130700     0.42680  \n",
       "mean concave points        0.020310    0.033500     0.074000     0.20120  \n",
       "mean symmetry              0.161900    0.179200     0.195700     0.30400  \n",
       "mean fractal dimension     0.057700    0.061540     0.066120     0.09744  \n",
       "radius error               0.232400    0.324200     0.478900     2.87300  \n",
       "texture error              0.833900    1.108000     1.474000     4.88500  \n",
       "perimeter error            1.606000    2.287000     3.357000    21.98000  \n",
       "area error                17.850000   24.530000    45.190000   542.20000  \n",
       "smoothness error           0.005169    0.006380     0.008146     0.03113  \n",
       "compactness error          0.013080    0.020450     0.032450     0.13540  \n",
       "concavity error            0.015090    0.025890     0.042050     0.39600  \n",
       "concave points error       0.007638    0.010930     0.014710     0.05279  \n",
       "symmetry error             0.015160    0.018730     0.023480     0.07895  \n",
       "fractal dimension error    0.002248    0.003187     0.004558     0.02984  \n",
       "worst radius              13.010000   14.970000    18.790000    36.04000  \n",
       "worst texture             21.080000   25.410000    29.720000    49.54000  \n",
       "worst perimeter           84.110000   97.660000   125.400000   251.20000  \n",
       "worst area               515.300000  686.500000  1084.000000  4254.00000  \n",
       "worst smoothness           0.116600    0.131300     0.146000     0.22260  \n",
       "worst compactness          0.147200    0.211900     0.339100     1.05800  \n",
       "worst concavity            0.114500    0.226700     0.382900     1.25200  \n",
       "worst concave points       0.064930    0.099930     0.161400     0.29100  \n",
       "worst symmetry             0.250400    0.282200     0.317900     0.66380  \n",
       "worst fractal dimension    0.071460    0.080040     0.092080     0.20750  \n",
       "benign_0__mal_1            0.000000    1.000000     1.000000     1.00000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.isnull().sum()\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bd89150>"
      ]
     },
     "execution_count": 7,
     "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": [
    "sns.countplot(x='benign_0__mal_1',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11dddc7d0>"
      ]
     },
     "execution_count": 8,
     "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": [
    "df.corr()['benign_0__mal_1'][:-1].sort_values().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b518fd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "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\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "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": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426, 30)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(30,activation='relu'))\n",
    "model.add(Dense(15,activation='relu'))\n",
    "model.add(Dense(1,activation='sigmoid'))\n",
    "model.compile(optimizer='adam',loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 426 samples, validate on 143 samples\n",
      "Epoch 1/600\n",
      "426/426 [==============================] - 1s 3ms/sample - loss: 0.6874 - val_loss: 0.6751\n",
      "Epoch 2/600\n",
      "426/426 [==============================] - 0s 140us/sample - loss: 0.6559 - val_loss: 0.6449\n",
      "Epoch 3/600\n",
      "426/426 [==============================] - 0s 146us/sample - loss: 0.6231 - val_loss: 0.6070\n",
      "Epoch 4/600\n",
      "426/426 [==============================] - 0s 150us/sample - loss: 0.5803 - val_loss: 0.5579\n",
      "Epoch 5/600\n",
      "426/426 [==============================] - 0s 167us/sample - loss: 0.5289 - val_loss: 0.4996\n",
      "Epoch 6/600\n",
      "426/426 [==============================] - 0s 148us/sample - loss: 0.4752 - val_loss: 0.4447\n",
      "Epoch 7/600\n",
      "426/426 [==============================] - 0s 230us/sample - loss: 0.4226 - val_loss: 0.3921\n",
      "Epoch 8/600\n",
      "426/426 [==============================] - 0s 124us/sample - loss: 0.3751 - val_loss: 0.3435\n",
      "Epoch 9/600\n",
      "426/426 [==============================] - 0s 137us/sample - loss: 0.3357 - val_loss: 0.3049\n",
      "Epoch 10/600\n",
      "426/426 [==============================] - 0s 137us/sample - loss: 0.3012 - val_loss: 0.2753\n",
      "Epoch 11/600\n",
      "426/426 [==============================] - 0s 153us/sample - loss: 0.2757 - val_loss: 0.2504\n",
      "Epoch 12/600\n",
      "426/426 [==============================] - 0s 303us/sample - loss: 0.2523 - val_loss: 0.2271\n",
      "Epoch 13/600\n",
      "426/426 [==============================] - 0s 346us/sample - loss: 0.2337 - val_loss: 0.2098\n",
      "Epoch 14/600\n",
      "426/426 [==============================] - 0s 312us/sample - loss: 0.2163 - val_loss: 0.1959\n",
      "Epoch 15/600\n",
      "426/426 [==============================] - 0s 181us/sample - loss: 0.2047 - val_loss: 0.1888\n",
      "Epoch 16/600\n",
      "426/426 [==============================] - 0s 199us/sample - loss: 0.1907 - val_loss: 0.1741\n",
      "Epoch 17/600\n",
      "426/426 [==============================] - 0s 138us/sample - loss: 0.1802 - val_loss: 0.1664\n",
      "Epoch 18/600\n",
      "426/426 [==============================] - 0s 121us/sample - loss: 0.1710 - val_loss: 0.1577\n",
      "Epoch 19/600\n",
      "426/426 [==============================] - 0s 128us/sample - loss: 0.1618 - val_loss: 0.1507\n",
      "Epoch 20/600\n",
      "426/426 [==============================] - 0s 156us/sample - loss: 0.1570 - val_loss: 0.1472\n",
      "Epoch 21/600\n",
      "426/426 [==============================] - 0s 139us/sample - loss: 0.1502 - val_loss: 0.1463\n",
      "Epoch 22/600\n",
      "426/426 [==============================] - 0s 134us/sample - loss: 0.1383 - val_loss: 0.1373\n",
      "Epoch 23/600\n",
      "426/426 [==============================] - 0s 159us/sample - loss: 0.1311 - val_loss: 0.1326\n",
      "Epoch 24/600\n",
      "426/426 [==============================] - 0s 127us/sample - loss: 0.1276 - val_loss: 0.1309\n",
      "Epoch 25/600\n",
      "426/426 [==============================] - 0s 123us/sample - loss: 0.1207 - val_loss: 0.1250\n",
      "Epoch 26/600\n",
      "426/426 [==============================] - 0s 126us/sample - loss: 0.1159 - val_loss: 0.1289\n",
      "Epoch 27/600\n",
      "426/426 [==============================] - 0s 126us/sample - loss: 0.1141 - val_loss: 0.1248\n",
      "Epoch 28/600\n",
      "426/426 [==============================] - 0s 113us/sample - loss: 0.1075 - val_loss: 0.1203\n",
      "Epoch 29/600\n",
      "426/426 [==============================] - 0s 126us/sample - loss: 0.1033 - val_loss: 0.1163\n",
      "Epoch 30/600\n",
      "426/426 [==============================] - 0s 120us/sample - loss: 0.1003 - val_loss: 0.1190\n",
      "Epoch 31/600\n",
      "426/426 [==============================] - 0s 140us/sample - loss: 0.0969 - val_loss: 0.1175\n",
      "Epoch 32/600\n",
      "426/426 [==============================] - 0s 145us/sample - loss: 0.0941 - val_loss: 0.1169\n",
      "Epoch 33/600\n",
      "426/426 [==============================] - 0s 147us/sample - loss: 0.0944 - val_loss: 0.1194\n",
      "Epoch 34/600\n",
      "426/426 [==============================] - 0s 175us/sample - loss: 0.0910 - val_loss: 0.1122\n",
      "Epoch 35/600\n",
      "426/426 [==============================] - 0s 141us/sample - loss: 0.0860 - val_loss: 0.1120\n",
      "Epoch 36/600\n",
      "426/426 [==============================] - 0s 151us/sample - loss: 0.0837 - val_loss: 0.1106\n",
      "Epoch 37/600\n",
      "426/426 [==============================] - 0s 147us/sample - loss: 0.0826 - val_loss: 0.1133\n",
      "Epoch 38/600\n",
      "426/426 [==============================] - 0s 129us/sample - loss: 0.0798 - val_loss: 0.1089\n",
      "Epoch 39/600\n",
      "426/426 [==============================] - 0s 133us/sample - loss: 0.0788 - val_loss: 0.1094\n",
      "Epoch 40/600\n",
      "426/426 [==============================] - 0s 117us/sample - loss: 0.0770 - val_loss: 0.1107\n",
      "Epoch 41/600\n",
      "426/426 [==============================] - 0s 124us/sample - loss: 0.0774 - val_loss: 0.1094\n",
      "Epoch 42/600\n",
      "426/426 [==============================] - 0s 119us/sample - loss: 0.0734 - val_loss: 0.1068\n",
      "Epoch 43/600\n",
      "426/426 [==============================] - 0s 125us/sample - loss: 0.0739 - val_loss: 0.1131\n",
      "Epoch 44/600\n",
      "426/426 [==============================] - 0s 115us/sample - loss: 0.0884 - val_loss: 0.1040\n",
      "Epoch 45/600\n",
      "426/426 [==============================] - 0s 133us/sample - loss: 0.0732 - val_loss: 0.1083\n",
      "Epoch 46/600\n",
      "426/426 [==============================] - 0s 114us/sample - loss: 0.0714 - val_loss: 0.1095\n",
      "Epoch 47/600\n",
      "426/426 [==============================] - 0s 119us/sample - loss: 0.0683 - val_loss: 0.1102\n",
      "Epoch 48/600\n",
      "426/426 [==============================] - 0s 120us/sample - loss: 0.0694 - val_loss: 0.1105\n",
      "Epoch 49/600\n",
      "426/426 [==============================] - 0s 117us/sample - loss: 0.0661 - val_loss: 0.1068\n",
      "Epoch 50/600\n",
      "426/426 [==============================] - 0s 118us/sample - loss: 0.0664 - val_loss: 0.1079\n",
      "Epoch 51/600\n",
      "426/426 [==============================] - 0s 132us/sample - loss: 0.0659 - val_loss: 0.1067\n",
      "Epoch 52/600\n",
      "426/426 [==============================] - 0s 127us/sample - loss: 0.0641 - val_loss: 0.1070\n",
      "Epoch 53/600\n",
      "426/426 [==============================] - 0s 118us/sample - loss: 0.0642 - val_loss: 0.1079\n",
      "Epoch 54/600\n",
      "426/426 [==============================] - 0s 119us/sample - loss: 0.0633 - val_loss: 0.1073\n",
      "Epoch 55/600\n",
      "426/426 [==============================] - 0s 114us/sample - loss: 0.0620 - val_loss: 0.1113\n",
      "Epoch 56/600\n",
      "426/426 [==============================] - 0s 123us/sample - loss: 0.0617 - val_loss: 0.1071\n",
      "Epoch 57/600\n",
      "426/426 [==============================] - 0s 114us/sample - loss: 0.0616 - val_loss: 0.1084\n",
      "Epoch 58/600\n",
      "426/426 [==============================] - 0s 115us/sample - loss: 0.0615 - val_loss: 0.1079\n",
      "Epoch 59/600\n",
      "426/426 [==============================] - 0s 115us/sample - loss: 0.0597 - val_loss: 0.1050\n",
      "Epoch 60/600\n",
      "426/426 [==============================] - 0s 117us/sample - loss: 0.0596 - val_loss: 0.1108\n",
      "Epoch 61/600\n",
      "426/426 [==============================] - 0s 135us/sample - loss: 0.0596 - val_loss: 0.1092\n",
      "Epoch 62/600\n",
      "426/426 [==============================] - 0s 137us/sample - loss: 0.0605 - val_loss: 0.1105\n",
      "Epoch 63/600\n",
      "426/426 [==============================] - 0s 142us/sample - loss: 0.0582 - val_loss: 0.1087\n",
      "Epoch 64/600\n",
      "426/426 [==============================] - 0s 149us/sample - loss: 0.0580 - val_loss: 0.1099\n",
      "Epoch 65/600\n",
      "426/426 [==============================] - 0s 123us/sample - loss: 0.0572 - val_loss: 0.1088\n",
      "Epoch 66/600\n",
      "426/426 [==============================] - 0s 114us/sample - loss: 0.0585 - val_loss: 0.1085\n",
      "Epoch 67/600\n",
      "426/426 [==============================] - 0s 124us/sample - loss: 0.0569 - val_loss: 0.1074\n",
      "Epoch 68/600\n",
      "426/426 [==============================] - 0s 119us/sample - loss: 0.0581 - val_loss: 0.1111\n",
      "Epoch 69/600\n",
      "426/426 [==============================] - 0s 114us/sample - loss: 0.0573 - val_loss: 0.1096\n",
      "Epoch 00069: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x138b9aa50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=X_train,y=y_train,epochs=600,validation_data=(X_test,y_test),callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_loss = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138f27510>"
      ]
     },
     "execution_count": 27,
     "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": [
    "model_loss.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Adding Dropout to prevent Overfitting</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Another Model</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(30,activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(15,activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1,activation='sigmoid'))\n",
    "model.compile(optimizer='adam',loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 426 samples, validate on 143 samples\n",
      "Epoch 1/600\n",
      "426/426 [==============================] - 1s 2ms/sample - loss: 0.7121 - val_loss: 0.6624\n",
      "Epoch 2/600\n",
      "426/426 [==============================] - 0s 161us/sample - loss: 0.6818 - val_loss: 0.6465\n",
      "Epoch 3/600\n",
      "426/426 [==============================] - 0s 200us/sample - loss: 0.6583 - val_loss: 0.6337\n",
      "Epoch 4/600\n",
      "426/426 [==============================] - 0s 266us/sample - loss: 0.6497 - val_loss: 0.6216\n",
      "Epoch 5/600\n",
      "426/426 [==============================] - 0s 199us/sample - loss: 0.6296 - val_loss: 0.6071\n",
      "Epoch 6/600\n",
      "426/426 [==============================] - 0s 148us/sample - loss: 0.6025 - val_loss: 0.5900\n",
      "Epoch 7/600\n",
      "426/426 [==============================] - 0s 136us/sample - loss: 0.6003 - val_loss: 0.5712\n",
      "Epoch 8/600\n",
      "426/426 [==============================] - 0s 130us/sample - loss: 0.5881 - val_loss: 0.5519\n",
      "Epoch 9/600\n",
      "426/426 [==============================] - 0s 155us/sample - loss: 0.5683 - val_loss: 0.5270\n",
      "Epoch 10/600\n",
      "426/426 [==============================] - 0s 133us/sample - loss: 0.5603 - val_loss: 0.4996\n",
      "Epoch 11/600\n",
      "426/426 [==============================] - 0s 132us/sample - loss: 0.5186 - val_loss: 0.4753\n",
      "Epoch 12/600\n",
      "426/426 [==============================] - 0s 128us/sample - loss: 0.5096 - val_loss: 0.4465\n",
      "Epoch 13/600\n",
      "426/426 [==============================] - 0s 126us/sample - loss: 0.4957 - val_loss: 0.4172\n",
      "Epoch 14/600\n",
      "426/426 [==============================] - 0s 128us/sample - loss: 0.4514 - val_loss: 0.3894\n",
      "Epoch 15/600\n",
      "426/426 [==============================] - 0s 128us/sample - loss: 0.4353 - val_loss: 0.3668\n",
      "Epoch 16/600\n",
      "426/426 [==============================] - 0s 128us/sample - loss: 0.4359 - val_loss: 0.3414\n",
      "Epoch 17/600\n",
      "426/426 [==============================] - 0s 140us/sample - loss: 0.3760 - val_loss: 0.3204\n",
      "Epoch 18/600\n",
      "426/426 [==============================] - 0s 125us/sample - loss: 0.4042 - val_loss: 0.3034\n",
      "Epoch 19/600\n",
      "426/426 [==============================] - 0s 134us/sample - loss: 0.3466 - val_loss: 0.2854\n",
      "Epoch 20/600\n",
      "426/426 [==============================] - 0s 145us/sample - loss: 0.3478 - val_loss: 0.2646\n",
      "Epoch 21/600\n",
      "426/426 [==============================] - 0s 119us/sample - loss: 0.3491 - val_loss: 0.2504\n",
      "Epoch 22/600\n",
      "426/426 [==============================] - 0s 124us/sample - loss: 0.3721 - val_loss: 0.2470\n",
      "Epoch 23/600\n",
      "426/426 [==============================] - 0s 118us/sample - loss: 0.3397 - val_loss: 0.2344\n",
      "Epoch 24/600\n",
      "426/426 [==============================] - 0s 122us/sample - loss: 0.3033 - val_loss: 0.2250\n",
      "Epoch 25/600\n",
      "426/426 [==============================] - 0s 118us/sample - loss: 0.2841 - val_loss: 0.2097\n",
      "Epoch 26/600\n",
      "426/426 [==============================] - 0s 163us/sample - loss: 0.2996 - val_loss: 0.1990\n",
      "Epoch 27/600\n",
      "426/426 [==============================] - 0s 149us/sample - loss: 0.2810 - val_loss: 0.1875\n",
      "Epoch 28/600\n",
      "426/426 [==============================] - 0s 130us/sample - loss: 0.2799 - val_loss: 0.1776\n",
      "Epoch 29/600\n",
      "426/426 [==============================] - 0s 123us/sample - loss: 0.2778 - val_loss: 0.1770\n",
      "Epoch 30/600\n",
      "426/426 [==============================] - 0s 117us/sample - loss: 0.2449 - val_loss: 0.1721\n",
      "Epoch 31/600\n",
      "426/426 [==============================] - 0s 127us/sample - loss: 0.2628 - val_loss: 0.1660\n",
      "Epoch 32/600\n",
      "426/426 [==============================] - 0s 120us/sample - loss: 0.2624 - val_loss: 0.1620\n",
      "Epoch 33/600\n",
      "426/426 [==============================] - 0s 157us/sample - loss: 0.2492 - val_loss: 0.1612\n",
      "Epoch 34/600\n",
      "426/426 [==============================] - 0s 141us/sample - loss: 0.2470 - val_loss: 0.1524\n",
      "Epoch 35/600\n",
      "426/426 [==============================] - 0s 147us/sample - loss: 0.2301 - val_loss: 0.1494\n",
      "Epoch 36/600\n",
      "426/426 [==============================] - 0s 156us/sample - loss: 0.2435 - val_loss: 0.1493\n",
      "Epoch 37/600\n",
      "426/426 [==============================] - 0s 227us/sample - loss: 0.2540 - val_loss: 0.1456\n",
      "Epoch 38/600\n",
      "426/426 [==============================] - 0s 129us/sample - loss: 0.2355 - val_loss: 0.1414\n",
      "Epoch 39/600\n",
      "426/426 [==============================] - 0s 127us/sample - loss: 0.2217 - val_loss: 0.1381\n",
      "Epoch 40/600\n",
      "426/426 [==============================] - 0s 125us/sample - loss: 0.2293 - val_loss: 0.1382\n",
      "Epoch 41/600\n",
      "426/426 [==============================] - 0s 128us/sample - loss: 0.2430 - val_loss: 0.1429\n",
      "Epoch 42/600\n",
      "426/426 [==============================] - 0s 124us/sample - loss: 0.2164 - val_loss: 0.1327\n",
      "Epoch 43/600\n",
      "426/426 [==============================] - 0s 114us/sample - loss: 0.2336 - val_loss: 0.1407\n",
      "Epoch 44/600\n",
      "426/426 [==============================] - 0s 124us/sample - loss: 0.2084 - val_loss: 0.1406\n",
      "Epoch 45/600\n",
      "426/426 [==============================] - 0s 117us/sample - loss: 0.2185 - val_loss: 0.1287\n",
      "Epoch 46/600\n",
      "426/426 [==============================] - 0s 123us/sample - loss: 0.2101 - val_loss: 0.1286\n",
      "Epoch 47/600\n",
      "426/426 [==============================] - 0s 111us/sample - loss: 0.1904 - val_loss: 0.1300\n",
      "Epoch 48/600\n",
      "426/426 [==============================] - 0s 132us/sample - loss: 0.2063 - val_loss: 0.1186\n",
      "Epoch 49/600\n",
      "426/426 [==============================] - 0s 119us/sample - loss: 0.1804 - val_loss: 0.1299\n",
      "Epoch 50/600\n",
      "426/426 [==============================] - 0s 131us/sample - loss: 0.1928 - val_loss: 0.1196\n",
      "Epoch 51/600\n",
      "426/426 [==============================] - 0s 124us/sample - loss: 0.1911 - val_loss: 0.1166\n",
      "Epoch 52/600\n",
      "426/426 [==============================] - 0s 124us/sample - loss: 0.1893 - val_loss: 0.1119\n",
      "Epoch 53/600\n",
      "426/426 [==============================] - 0s 126us/sample - loss: 0.1634 - val_loss: 0.1110\n",
      "Epoch 54/600\n",
      "426/426 [==============================] - 0s 119us/sample - loss: 0.1943 - val_loss: 0.1241\n",
      "Epoch 55/600\n",
      "426/426 [==============================] - 0s 135us/sample - loss: 0.1709 - val_loss: 0.1179\n",
      "Epoch 56/600\n",
      "426/426 [==============================] - 0s 118us/sample - loss: 0.1738 - val_loss: 0.1098\n",
      "Epoch 57/600\n",
      "426/426 [==============================] - 0s 140us/sample - loss: 0.1624 - val_loss: 0.1093\n",
      "Epoch 58/600\n",
      "426/426 [==============================] - 0s 123us/sample - loss: 0.1642 - val_loss: 0.1099\n",
      "Epoch 59/600\n",
      "426/426 [==============================] - 0s 269us/sample - loss: 0.1502 - val_loss: 0.1025\n",
      "Epoch 60/600\n",
      "426/426 [==============================] - 0s 246us/sample - loss: 0.1712 - val_loss: 0.1086\n",
      "Epoch 61/600\n",
      "426/426 [==============================] - 0s 180us/sample - loss: 0.1609 - val_loss: 0.1078\n",
      "Epoch 62/600\n",
      "426/426 [==============================] - 0s 141us/sample - loss: 0.1610 - val_loss: 0.0986\n",
      "Epoch 63/600\n",
      "426/426 [==============================] - 0s 142us/sample - loss: 0.1587 - val_loss: 0.1098\n",
      "Epoch 64/600\n",
      "426/426 [==============================] - 0s 218us/sample - loss: 0.1592 - val_loss: 0.1026\n",
      "Epoch 65/600\n",
      "426/426 [==============================] - 0s 162us/sample - loss: 0.1416 - val_loss: 0.1058\n",
      "Epoch 66/600\n",
      "426/426 [==============================] - 0s 167us/sample - loss: 0.1503 - val_loss: 0.1277\n",
      "Epoch 67/600\n",
      "426/426 [==============================] - 0s 162us/sample - loss: 0.1439 - val_loss: 0.1013\n",
      "Epoch 68/600\n",
      "426/426 [==============================] - 0s 154us/sample - loss: 0.1526 - val_loss: 0.1026\n",
      "Epoch 69/600\n",
      "426/426 [==============================] - 0s 130us/sample - loss: 0.1291 - val_loss: 0.0956\n",
      "Epoch 70/600\n",
      "426/426 [==============================] - 0s 141us/sample - loss: 0.1412 - val_loss: 0.0945\n",
      "Epoch 71/600\n",
      "426/426 [==============================] - 0s 128us/sample - loss: 0.1447 - val_loss: 0.0985\n",
      "Epoch 72/600\n",
      "426/426 [==============================] - 0s 145us/sample - loss: 0.1290 - val_loss: 0.0935\n",
      "Epoch 73/600\n",
      "426/426 [==============================] - 0s 148us/sample - loss: 0.1288 - val_loss: 0.0947\n",
      "Epoch 74/600\n",
      "426/426 [==============================] - 0s 344us/sample - loss: 0.1243 - val_loss: 0.0888\n",
      "Epoch 75/600\n",
      "426/426 [==============================] - 0s 251us/sample - loss: 0.1382 - val_loss: 0.0943\n",
      "Epoch 76/600\n",
      "426/426 [==============================] - 0s 170us/sample - loss: 0.1262 - val_loss: 0.1023\n",
      "Epoch 77/600\n",
      "426/426 [==============================] - 0s 157us/sample - loss: 0.1192 - val_loss: 0.0979\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 78/600\n",
      "426/426 [==============================] - 0s 198us/sample - loss: 0.1463 - val_loss: 0.1023\n",
      "Epoch 79/600\n",
      "426/426 [==============================] - 0s 213us/sample - loss: 0.1522 - val_loss: 0.0923\n",
      "Epoch 80/600\n",
      "426/426 [==============================] - 0s 121us/sample - loss: 0.1322 - val_loss: 0.0988\n",
      "Epoch 81/600\n",
      "426/426 [==============================] - 0s 116us/sample - loss: 0.1374 - val_loss: 0.0918\n",
      "Epoch 82/600\n",
      "426/426 [==============================] - 0s 120us/sample - loss: 0.1407 - val_loss: 0.0975\n",
      "Epoch 83/600\n",
      "426/426 [==============================] - 0s 111us/sample - loss: 0.1298 - val_loss: 0.0923\n",
      "Epoch 84/600\n",
      "426/426 [==============================] - 0s 123us/sample - loss: 0.1440 - val_loss: 0.0983\n",
      "Epoch 85/600\n",
      "426/426 [==============================] - 0s 120us/sample - loss: 0.1369 - val_loss: 0.1061\n",
      "Epoch 86/600\n",
      "426/426 [==============================] - 0s 106us/sample - loss: 0.1375 - val_loss: 0.0877\n",
      "Epoch 87/600\n",
      "426/426 [==============================] - 0s 123us/sample - loss: 0.1128 - val_loss: 0.0969\n",
      "Epoch 88/600\n",
      "426/426 [==============================] - 0s 103us/sample - loss: 0.1044 - val_loss: 0.0982\n",
      "Epoch 89/600\n",
      "426/426 [==============================] - 0s 104us/sample - loss: 0.1222 - val_loss: 0.0849\n",
      "Epoch 90/600\n",
      "426/426 [==============================] - 0s 105us/sample - loss: 0.1322 - val_loss: 0.0971\n",
      "Epoch 91/600\n",
      "426/426 [==============================] - 0s 105us/sample - loss: 0.1471 - val_loss: 0.0899\n",
      "Epoch 92/600\n",
      "426/426 [==============================] - 0s 105us/sample - loss: 0.1402 - val_loss: 0.0862\n",
      "Epoch 93/600\n",
      "426/426 [==============================] - 0s 107us/sample - loss: 0.1092 - val_loss: 0.0961\n",
      "Epoch 94/600\n",
      "426/426 [==============================] - 0s 105us/sample - loss: 0.1130 - val_loss: 0.1036\n",
      "Epoch 95/600\n",
      "426/426 [==============================] - 0s 107us/sample - loss: 0.1321 - val_loss: 0.0849\n",
      "Epoch 96/600\n",
      "426/426 [==============================] - 0s 107us/sample - loss: 0.1199 - val_loss: 0.0978\n",
      "Epoch 97/600\n",
      "426/426 [==============================] - 0s 102us/sample - loss: 0.1176 - val_loss: 0.1103\n",
      "Epoch 98/600\n",
      "426/426 [==============================] - 0s 100us/sample - loss: 0.1085 - val_loss: 0.0878\n",
      "Epoch 99/600\n",
      "426/426 [==============================] - 0s 103us/sample - loss: 0.1206 - val_loss: 0.0824\n",
      "Epoch 100/600\n",
      "426/426 [==============================] - 0s 104us/sample - loss: 0.1147 - val_loss: 0.1060\n",
      "Epoch 101/600\n",
      "426/426 [==============================] - 0s 104us/sample - loss: 0.1029 - val_loss: 0.0912\n",
      "Epoch 102/600\n",
      "426/426 [==============================] - 0s 105us/sample - loss: 0.1186 - val_loss: 0.0881\n",
      "Epoch 103/600\n",
      "426/426 [==============================] - 0s 108us/sample - loss: 0.1004 - val_loss: 0.1013\n",
      "Epoch 104/600\n",
      "426/426 [==============================] - 0s 108us/sample - loss: 0.1022 - val_loss: 0.0880\n",
      "Epoch 105/600\n",
      "426/426 [==============================] - 0s 107us/sample - loss: 0.0945 - val_loss: 0.0846\n",
      "Epoch 106/600\n",
      "426/426 [==============================] - 0s 110us/sample - loss: 0.1347 - val_loss: 0.0956\n",
      "Epoch 107/600\n",
      "426/426 [==============================] - 0s 108us/sample - loss: 0.1045 - val_loss: 0.0854\n",
      "Epoch 108/600\n",
      "426/426 [==============================] - 0s 112us/sample - loss: 0.1127 - val_loss: 0.0927\n",
      "Epoch 109/600\n",
      "426/426 [==============================] - 0s 110us/sample - loss: 0.1132 - val_loss: 0.0899\n",
      "Epoch 110/600\n",
      "426/426 [==============================] - 0s 112us/sample - loss: 0.1053 - val_loss: 0.0940\n",
      "Epoch 111/600\n",
      "426/426 [==============================] - 0s 108us/sample - loss: 0.1038 - val_loss: 0.0935\n",
      "Epoch 112/600\n",
      "426/426 [==============================] - 0s 110us/sample - loss: 0.1137 - val_loss: 0.1001\n",
      "Epoch 113/600\n",
      "426/426 [==============================] - 0s 105us/sample - loss: 0.1155 - val_loss: 0.0909\n",
      "Epoch 114/600\n",
      "426/426 [==============================] - 0s 107us/sample - loss: 0.1131 - val_loss: 0.0980\n",
      "Epoch 115/600\n",
      "426/426 [==============================] - 0s 107us/sample - loss: 0.1220 - val_loss: 0.0874\n",
      "Epoch 116/600\n",
      "426/426 [==============================] - 0s 112us/sample - loss: 0.0984 - val_loss: 0.0952\n",
      "Epoch 117/600\n",
      "426/426 [==============================] - 0s 108us/sample - loss: 0.1157 - val_loss: 0.1022\n",
      "Epoch 118/600\n",
      "426/426 [==============================] - 0s 106us/sample - loss: 0.0917 - val_loss: 0.1063\n",
      "Epoch 119/600\n",
      "426/426 [==============================] - 0s 156us/sample - loss: 0.1108 - val_loss: 0.0827\n",
      "Epoch 120/600\n",
      "426/426 [==============================] - 0s 128us/sample - loss: 0.0991 - val_loss: 0.0982\n",
      "Epoch 121/600\n",
      "426/426 [==============================] - 0s 130us/sample - loss: 0.0956 - val_loss: 0.0969\n",
      "Epoch 122/600\n",
      "426/426 [==============================] - 0s 134us/sample - loss: 0.1081 - val_loss: 0.0840\n",
      "Epoch 123/600\n",
      "426/426 [==============================] - 0s 119us/sample - loss: 0.0984 - val_loss: 0.0810\n",
      "Epoch 124/600\n",
      "426/426 [==============================] - 0s 118us/sample - loss: 0.0949 - val_loss: 0.1085\n",
      "Epoch 125/600\n",
      "426/426 [==============================] - 0s 129us/sample - loss: 0.1062 - val_loss: 0.0859\n",
      "Epoch 126/600\n",
      "426/426 [==============================] - 0s 127us/sample - loss: 0.1063 - val_loss: 0.0942\n",
      "Epoch 127/600\n",
      "426/426 [==============================] - 0s 129us/sample - loss: 0.0996 - val_loss: 0.0856\n",
      "Epoch 128/600\n",
      "426/426 [==============================] - 0s 146us/sample - loss: 0.1001 - val_loss: 0.0863\n",
      "Epoch 129/600\n",
      "426/426 [==============================] - 0s 125us/sample - loss: 0.0915 - val_loss: 0.0907\n",
      "Epoch 130/600\n",
      "426/426 [==============================] - 0s 145us/sample - loss: 0.0939 - val_loss: 0.0851\n",
      "Epoch 131/600\n",
      "426/426 [==============================] - 0s 149us/sample - loss: 0.0893 - val_loss: 0.1004\n",
      "Epoch 132/600\n",
      "426/426 [==============================] - 0s 130us/sample - loss: 0.0824 - val_loss: 0.0973\n",
      "Epoch 133/600\n",
      "426/426 [==============================] - 0s 124us/sample - loss: 0.0980 - val_loss: 0.0862\n",
      "Epoch 134/600\n",
      "426/426 [==============================] - 0s 133us/sample - loss: 0.0992 - val_loss: 0.0846\n",
      "Epoch 135/600\n",
      "426/426 [==============================] - 0s 130us/sample - loss: 0.0980 - val_loss: 0.0867\n",
      "Epoch 136/600\n",
      "426/426 [==============================] - 0s 149us/sample - loss: 0.1014 - val_loss: 0.0860\n",
      "Epoch 137/600\n",
      "426/426 [==============================] - 0s 112us/sample - loss: 0.1118 - val_loss: 0.0969\n",
      "Epoch 138/600\n",
      "426/426 [==============================] - 0s 114us/sample - loss: 0.0973 - val_loss: 0.0891\n",
      "Epoch 139/600\n",
      "426/426 [==============================] - 0s 115us/sample - loss: 0.0983 - val_loss: 0.1020\n",
      "Epoch 140/600\n",
      "426/426 [==============================] - 0s 125us/sample - loss: 0.0850 - val_loss: 0.0883\n",
      "Epoch 141/600\n",
      "426/426 [==============================] - 0s 109us/sample - loss: 0.1036 - val_loss: 0.0951\n",
      "Epoch 142/600\n",
      "426/426 [==============================] - 0s 111us/sample - loss: 0.0981 - val_loss: 0.0877\n",
      "Epoch 143/600\n",
      "426/426 [==============================] - 0s 117us/sample - loss: 0.0850 - val_loss: 0.1019\n",
      "Epoch 144/600\n",
      "426/426 [==============================] - 0s 106us/sample - loss: 0.0857 - val_loss: 0.0872\n",
      "Epoch 145/600\n",
      "426/426 [==============================] - 0s 112us/sample - loss: 0.0954 - val_loss: 0.0899\n",
      "Epoch 146/600\n",
      "426/426 [==============================] - 0s 103us/sample - loss: 0.0811 - val_loss: 0.1080\n",
      "Epoch 147/600\n",
      "426/426 [==============================] - 0s 103us/sample - loss: 0.0862 - val_loss: 0.0878\n",
      "Epoch 148/600\n",
      "426/426 [==============================] - 0s 103us/sample - loss: 0.0886 - val_loss: 0.1056\n",
      "Epoch 00148: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1394f76d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=X_train,y=y_train,epochs=600,validation_data=(X_test,y_test),callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_loss = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13a293c10>"
      ]
     },
     "execution_count": 31,
     "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": [
    "model_loss.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.98      0.96        55\n",
      "           1       0.99      0.95      0.97        88\n",
      "\n",
      "    accuracy                           0.97       143\n",
      "   macro avg       0.96      0.97      0.96       143\n",
      "weighted avg       0.97      0.97      0.97       143\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[54  1]\n",
      " [ 4 84]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
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