31-TensorflowSiniflandirma.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFrame = pd.read_excel(\"maliciousornot.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>URL_LENGTH</th>\n",
       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
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       "      <td>0.255699</td>\n",
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       "      <td>0.268004</td>\n",
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       "      <td>30.531007</td>\n",
       "      <td>33.069860</td>\n",
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       "      <td>2213.724667</td>\n",
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       "      <td>0.549869</td>\n",
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       "      <td>0.333548</td>\n",
       "      <td>0.859842</td>\n",
       "      <td>224.092667</td>\n",
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       "      <th>4</th>\n",
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       "      <td>256.476000</td>\n",
       "      <td>0.135103</td>\n",
       "      <td>0.234326</td>\n",
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       "      <td>0.315932</td>\n",
       "      <td>0.179922</td>\n",
       "      <td>0.223575</td>\n",
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       "      <td>32.966233</td>\n",
       "      <td>34.196800</td>\n",
       "      <td>215.154867</td>\n",
       "      <td>2625.640667</td>\n",
       "      <td>182.642000</td>\n",
       "      <td>0.273704</td>\n",
       "      <td>0.531993</td>\n",
       "      <td>0.287046</td>\n",
       "      <td>266.838667</td>\n",
       "      <td>0.092163</td>\n",
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       "      <th>544</th>\n",
       "      <td>1</td>\n",
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       "      <td>169.947733</td>\n",
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       "      <td>186.528000</td>\n",
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       "      <td>0.226942</td>\n",
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       "      <td>205.958000</td>\n",
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       "      <td>24.585427</td>\n",
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       "      <td>0.147538</td>\n",
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       "      <td>0.183678</td>\n",
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       "      <td>37.992127</td>\n",
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       "      <td>0.455180</td>\n",
       "      <td>196.890667</td>\n",
       "      <td>0.310491</td>\n",
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       "      <td>33.341880</td>\n",
       "      <td>51.062040</td>\n",
       "      <td>239.118533</td>\n",
       "      <td>2358.802000</td>\n",
       "      <td>213.730000</td>\n",
       "      <td>1.124479</td>\n",
       "      <td>1.215929</td>\n",
       "      <td>343.263333</td>\n",
       "      <td>0.529403</td>\n",
       "      <td>160.621333</td>\n",
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       "      <th>547</th>\n",
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       "      <td>62.072373</td>\n",
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       "      <td>0.116528</td>\n",
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       "      <td>0.371890</td>\n",
       "      <td>0.091179</td>\n",
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       "<p>548 rows × 31 columns</p>\n",
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      "text/plain": [
       "     Type  URL_LENGTH  NUMBER_SPECIAL_CHARACTERS  TCP_CONVERSATION_EXCHANGE  \\\n",
       "0       1   23.303047                  13.445560                 159.066933   \n",
       "1       1   26.645007                  23.018073                 172.149800   \n",
       "2       1   25.505113                  27.525833                 168.393333   \n",
       "3       1   14.792707                  26.398893                 100.491960   \n",
       "4       1   26.282313                  18.575080                 174.999533   \n",
       "..    ...         ...                        ...                        ...   \n",
       "543     1   27.927387                  29.002513                 183.937333   \n",
       "544     1   26.075060                  36.593167                 169.947733   \n",
       "545     1   21.502533                  36.372960                 140.284600   \n",
       "546     1   26.683867                  37.992127                 181.476200   \n",
       "547     0   10.051787                  31.787480                  62.072373   \n",
       "\n",
       "     DIST_REMOTE_TCP_PORT  REMOTE_IPS   APP_BYTES  SOURCE_APP_PACKETS  \\\n",
       "0             1296.628667    0.153367    0.359585            0.388730   \n",
       "1             1717.612000    0.109767    0.101865            0.112564   \n",
       "2             1558.286000    0.141969    0.207124            0.255699   \n",
       "3              500.128200    0.184585    0.367745            0.312693   \n",
       "4             1680.047333    0.129922    0.172020          256.476000   \n",
       "..                    ...         ...         ...                 ...   \n",
       "543           1915.798000  143.782000    0.150129            0.315932   \n",
       "544           1633.415333    0.126684    0.133937          186.528000   \n",
       "545           1111.525533    0.109520    0.132513            0.119831   \n",
       "546           1638.596667    0.152590  358.807333            0.455180   \n",
       "547            234.455333    0.068173    0.056502            0.000000   \n",
       "\n",
       "     REMOTE_APP_PACKETS  SOURCE_APP_BYTES  ...      SOURCE_I   SOURCE_J  \\\n",
       "0              0.190544          0.313341  ...     32.875560  22.448127   \n",
       "1              0.090894          0.234714  ...     32.370380  30.323753   \n",
       "2              0.165673          0.268004  ...     30.531007  33.069860   \n",
       "3              0.136269          0.336398  ...     19.313420  34.326333   \n",
       "4              0.135103          0.234326  ...     29.196813  21.593207   \n",
       "..                  ...               ...  ...           ...        ...   \n",
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       "544            0.126826          0.226942  ...     30.686447  49.546500   \n",
       "545            0.068679        205.958000  ...     24.585427  44.196773   \n",
       "546          196.890667          0.310491  ...     33.341880  51.062040   \n",
       "547            0.000000          0.205569  ...  12248.672000  39.339273   \n",
       "\n",
       "       SOURCE_K     SOURCE_M    SOURCE_L    SOURCE_N  SOURCE_O    SOURCE_P  \\\n",
       "0    239.118533  2615.278000    0.210103    0.862174  0.922148    0.343781   \n",
       "1    205.698933  2533.672000    0.160362    0.241709  0.312953  240.932000   \n",
       "2    197.538333  2213.724667    0.187046    0.549869  0.583418  314.766000   \n",
       "3    128.069607   735.360733    0.271761    1.122147  0.889764    0.333548   \n",
       "4    197.149733  2040.150000    0.177979  265.543333  0.518133    0.210492   \n",
       "..          ...          ...         ...         ...       ...         ...   \n",
       "543  215.154867  2625.640667  182.642000    0.273704  0.531993    0.287046   \n",
       "544  200.776667  2242.222000    0.151036    0.248963  0.416450    0.210880   \n",
       "545  164.118733  1455.954667    0.147538    0.400776  0.440802    0.183678   \n",
       "546  239.118533  2358.802000  213.730000    1.124479  1.215929  343.263333   \n",
       "547   76.631920   347.926533    0.116528    0.083471  0.000000    0.000000   \n",
       "\n",
       "       SOURCE_R    SOURCE_S  \n",
       "0      0.595983    0.154015  \n",
       "1    356.216667    0.115311  \n",
       "2      0.468004    0.113445  \n",
       "3      0.859842  224.092667  \n",
       "4      0.306217    0.099456  \n",
       "..          ...         ...  \n",
       "543  266.838667    0.092163  \n",
       "544    0.333160    0.085971  \n",
       "545    0.287305    0.101295  \n",
       "546    0.529403  160.621333  \n",
       "547    0.371890    0.091179  \n",
       "\n",
       "[548 rows x 31 columns]"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 548 entries, 0 to 547\n",
      "Data columns (total 31 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Type                       548 non-null    int64  \n",
      " 1   URL_LENGTH                 548 non-null    float64\n",
      " 2   NUMBER_SPECIAL_CHARACTERS  548 non-null    float64\n",
      " 3   TCP_CONVERSATION_EXCHANGE  548 non-null    float64\n",
      " 4   DIST_REMOTE_TCP_PORT       548 non-null    float64\n",
      " 5   REMOTE_IPS                 548 non-null    float64\n",
      " 6   APP_BYTES                  548 non-null    float64\n",
      " 7   SOURCE_APP_PACKETS         548 non-null    float64\n",
      " 8   REMOTE_APP_PACKETS         548 non-null    float64\n",
      " 9   SOURCE_APP_BYTES           548 non-null    float64\n",
      " 10  REMOTE_APP_BYTES           548 non-null    float64\n",
      " 11  APP_PACKETS                548 non-null    float64\n",
      " 12  DNS_QUERY_TIMES            548 non-null    float64\n",
      " 13  SOURCE_A                   548 non-null    float64\n",
      " 14  SOURCE_B                   548 non-null    float64\n",
      " 15  SOURCE_C                   548 non-null    float64\n",
      " 16  SOURCE_D                   548 non-null    float64\n",
      " 17  SOURCE_F                   548 non-null    float64\n",
      " 18  SOURCE_E                   548 non-null    float64\n",
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      " 29  SOURCE_R                   548 non-null    float64\n",
      " 30  SOURCE_S                   548 non-null    float64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 132.8 KB\n"
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   "execution_count": 5,
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>URL_LENGTH</th>\n",
       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
       "      <th>REMOTE_IPS</th>\n",
       "      <th>APP_BYTES</th>\n",
       "      <th>SOURCE_APP_PACKETS</th>\n",
       "      <th>REMOTE_APP_PACKETS</th>\n",
       "      <th>SOURCE_APP_BYTES</th>\n",
       "      <th>...</th>\n",
       "      <th>SOURCE_I</th>\n",
       "      <th>SOURCE_J</th>\n",
       "      <th>SOURCE_K</th>\n",
       "      <th>SOURCE_M</th>\n",
       "      <th>SOURCE_L</th>\n",
       "      <th>SOURCE_N</th>\n",
       "      <th>SOURCE_O</th>\n",
       "      <th>SOURCE_P</th>\n",
       "      <th>SOURCE_R</th>\n",
       "      <th>SOURCE_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "      <td>548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383212</td>\n",
       "      <td>949.973475</td>\n",
       "      <td>25.015747</td>\n",
       "      <td>119.725438</td>\n",
       "      <td>857.123249</td>\n",
       "      <td>5.785481</td>\n",
       "      <td>6.499737</td>\n",
       "      <td>10.069604</td>\n",
       "      <td>3.181365</td>\n",
       "      <td>22.809689</td>\n",
       "      <td>...</td>\n",
       "      <td>399.714125</td>\n",
       "      <td>33.295952</td>\n",
       "      <td>139.830855</td>\n",
       "      <td>1155.666380</td>\n",
       "      <td>14.295530</td>\n",
       "      <td>32.855845</td>\n",
       "      <td>34.913670</td>\n",
       "      <td>11.758580</td>\n",
       "      <td>40.829159</td>\n",
       "      <td>2.637820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486613</td>\n",
       "      <td>3202.802599</td>\n",
       "      <td>5.605685</td>\n",
       "      <td>31.842845</td>\n",
       "      <td>461.579998</td>\n",
       "      <td>27.796268</td>\n",
       "      <td>35.390031</td>\n",
       "      <td>46.976527</td>\n",
       "      <td>21.315640</td>\n",
       "      <td>70.942060</td>\n",
       "      <td>...</td>\n",
       "      <td>2117.405314</td>\n",
       "      <td>7.956699</td>\n",
       "      <td>44.017609</td>\n",
       "      <td>746.777340</td>\n",
       "      <td>49.075477</td>\n",
       "      <td>126.026487</td>\n",
       "      <td>149.880701</td>\n",
       "      <td>52.045464</td>\n",
       "      <td>119.531119</td>\n",
       "      <td>19.086225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.051787</td>\n",
       "      <td>12.577687</td>\n",
       "      <td>56.722647</td>\n",
       "      <td>185.880333</td>\n",
       "      <td>0.068173</td>\n",
       "      <td>0.025104</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.151165</td>\n",
       "      <td>...</td>\n",
       "      <td>10.271993</td>\n",
       "      <td>15.569907</td>\n",
       "      <td>65.297753</td>\n",
       "      <td>239.895733</td>\n",
       "      <td>0.105246</td>\n",
       "      <td>0.044456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.202720</td>\n",
       "      <td>0.071295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.838688</td>\n",
       "      <td>20.987638</td>\n",
       "      <td>97.470595</td>\n",
       "      <td>544.622900</td>\n",
       "      <td>0.112309</td>\n",
       "      <td>0.084718</td>\n",
       "      <td>0.038394</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.211755</td>\n",
       "      <td>...</td>\n",
       "      <td>17.082208</td>\n",
       "      <td>27.325057</td>\n",
       "      <td>108.999062</td>\n",
       "      <td>667.971017</td>\n",
       "      <td>0.154501</td>\n",
       "      <td>0.196275</td>\n",
       "      <td>0.152558</td>\n",
       "      <td>0.084805</td>\n",
       "      <td>0.331022</td>\n",
       "      <td>0.093099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.069900</td>\n",
       "      <td>24.423510</td>\n",
       "      <td>112.920683</td>\n",
       "      <td>723.637967</td>\n",
       "      <td>0.125084</td>\n",
       "      <td>0.122720</td>\n",
       "      <td>0.085395</td>\n",
       "      <td>0.045304</td>\n",
       "      <td>0.235168</td>\n",
       "      <td>...</td>\n",
       "      <td>19.838030</td>\n",
       "      <td>32.972710</td>\n",
       "      <td>127.117537</td>\n",
       "      <td>901.616767</td>\n",
       "      <td>0.173899</td>\n",
       "      <td>0.297149</td>\n",
       "      <td>0.328950</td>\n",
       "      <td>0.132318</td>\n",
       "      <td>0.374869</td>\n",
       "      <td>0.103743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.264187</td>\n",
       "      <td>28.270650</td>\n",
       "      <td>137.175800</td>\n",
       "      <td>1037.270550</td>\n",
       "      <td>0.137694</td>\n",
       "      <td>0.172215</td>\n",
       "      <td>0.189799</td>\n",
       "      <td>0.101201</td>\n",
       "      <td>0.266159</td>\n",
       "      <td>...</td>\n",
       "      <td>25.919620</td>\n",
       "      <td>38.698083</td>\n",
       "      <td>164.636867</td>\n",
       "      <td>1460.488333</td>\n",
       "      <td>0.194689</td>\n",
       "      <td>0.497797</td>\n",
       "      <td>0.559098</td>\n",
       "      <td>0.223866</td>\n",
       "      <td>0.430342</td>\n",
       "      <td>0.119375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12828.981333</td>\n",
       "      <td>50.880693</td>\n",
       "      <td>244.170333</td>\n",
       "      <td>3239.628667</td>\n",
       "      <td>159.326000</td>\n",
       "      <td>358.807333</td>\n",
       "      <td>405.439333</td>\n",
       "      <td>209.844000</td>\n",
       "      <td>393.781333</td>\n",
       "      <td>...</td>\n",
       "      <td>12928.722000</td>\n",
       "      <td>64.170813</td>\n",
       "      <td>325.387733</td>\n",
       "      <td>5510.348000</td>\n",
       "      <td>239.636667</td>\n",
       "      <td>1370.462667</td>\n",
       "      <td>1621.757333</td>\n",
       "      <td>376.942000</td>\n",
       "      <td>704.661333</td>\n",
       "      <td>224.092667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Type    URL_LENGTH  NUMBER_SPECIAL_CHARACTERS  \\\n",
       "count  548.000000    548.000000                 548.000000   \n",
       "mean     0.383212    949.973475                  25.015747   \n",
       "std      0.486613   3202.802599                   5.605685   \n",
       "min      0.000000     10.051787                  12.577687   \n",
       "25%      0.000000     15.838688                  20.987638   \n",
       "50%      0.000000     18.069900                  24.423510   \n",
       "75%      1.000000     23.264187                  28.270650   \n",
       "max      1.000000  12828.981333                  50.880693   \n",
       "\n",
       "       TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  REMOTE_IPS  \\\n",
       "count                 548.000000            548.000000  548.000000   \n",
       "mean                  119.725438            857.123249    5.785481   \n",
       "std                    31.842845            461.579998   27.796268   \n",
       "min                    56.722647            185.880333    0.068173   \n",
       "25%                    97.470595            544.622900    0.112309   \n",
       "50%                   112.920683            723.637967    0.125084   \n",
       "75%                   137.175800           1037.270550    0.137694   \n",
       "max                   244.170333           3239.628667  159.326000   \n",
       "\n",
       "        APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  SOURCE_APP_BYTES  \\\n",
       "count  548.000000          548.000000          548.000000        548.000000   \n",
       "mean     6.499737           10.069604            3.181365         22.809689   \n",
       "std     35.390031           46.976527           21.315640         70.942060   \n",
       "min      0.025104            0.000000            0.000000          0.151165   \n",
       "25%      0.084718            0.038394            0.026383          0.211755   \n",
       "50%      0.122720            0.085395            0.045304          0.235168   \n",
       "75%      0.172215            0.189799            0.101201          0.266159   \n",
       "max    358.807333          405.439333          209.844000        393.781333   \n",
       "\n",
       "       ...      SOURCE_I    SOURCE_J    SOURCE_K     SOURCE_M    SOURCE_L  \\\n",
       "count  ...    548.000000  548.000000  548.000000   548.000000  548.000000   \n",
       "mean   ...    399.714125   33.295952  139.830855  1155.666380   14.295530   \n",
       "std    ...   2117.405314    7.956699   44.017609   746.777340   49.075477   \n",
       "min    ...     10.271993   15.569907   65.297753   239.895733    0.105246   \n",
       "25%    ...     17.082208   27.325057  108.999062   667.971017    0.154501   \n",
       "50%    ...     19.838030   32.972710  127.117537   901.616767    0.173899   \n",
       "75%    ...     25.919620   38.698083  164.636867  1460.488333    0.194689   \n",
       "max    ...  12928.722000   64.170813  325.387733  5510.348000  239.636667   \n",
       "\n",
       "          SOURCE_N     SOURCE_O    SOURCE_P    SOURCE_R    SOURCE_S  \n",
       "count   548.000000   548.000000  548.000000  548.000000  548.000000  \n",
       "mean     32.855845    34.913670   11.758580   40.829159    2.637820  \n",
       "std     126.026487   149.880701   52.045464  119.531119   19.086225  \n",
       "min       0.044456     0.000000    0.000000    0.202720    0.071295  \n",
       "25%       0.196275     0.152558    0.084805    0.331022    0.093099  \n",
       "50%       0.297149     0.328950    0.132318    0.374869    0.103743  \n",
       "75%       0.497797     0.559098    0.223866    0.430342    0.119375  \n",
       "max    1370.462667  1621.757333  376.942000  704.661333  224.092667  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "URL_LENGTH                  -0.228422\n",
       "SOURCE_I                    -0.138708\n",
       "SOURCE_B                    -0.128587\n",
       "SOURCE_APP_BYTES            -0.086080\n",
       "SOURCE_C                    -0.075369\n",
       "REMOTE_APP_BYTES            -0.048806\n",
       "SOURCE_G                    -0.017433\n",
       "DNS_QUERY_TIMES             -0.011055\n",
       "SOURCE_F                    -0.007551\n",
       "SOURCE_E                     0.001985\n",
       "SOURCE_L                     0.022932\n",
       "SOURCE_D                     0.029479\n",
       "SOURCE_H                     0.055045\n",
       "SOURCE_O                     0.063622\n",
       "SOURCE_R                     0.069140\n",
       "SOURCE_N                     0.088076\n",
       "APP_BYTES                    0.096659\n",
       "REMOTE_IPS                   0.126232\n",
       "SOURCE_APP_PACKETS           0.129433\n",
       "REMOTE_APP_PACKETS           0.139874\n",
       "SOURCE_S                     0.141134\n",
       "SOURCE_P                     0.205141\n",
       "APP_PACKETS                  0.240818\n",
       "NUMBER_SPECIAL_CHARACTERS    0.412095\n",
       "SOURCE_J                     0.453197\n",
       "SOURCE_A                     0.536539\n",
       "DIST_REMOTE_TCP_PORT         0.710294\n",
       "SOURCE_M                     0.734002\n",
       "TCP_CONVERSATION_EXCHANGE    0.744570\n",
       "SOURCE_K                     0.784173\n",
       "Type                         1.000000\n",
       "Name: Type, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.corr()[\"Type\"].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sbn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2807b99d0>"
      ]
     },
     "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": [
    "sbn.countplot(x=\"Type\", data = dataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd241105f50>"
      ]
     },
     "execution_count": 9,
     "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": [
    "dataFrame.corr()[\"Type\"].sort_values().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataFrame[\"Type\"].values\n",
    "x = dataFrame.drop(\"Type\",axis = 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=0.3,random_state=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler(copy=True, feature_range=(0, 1))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = scaler.transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation, Dropout\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(383, 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(units=30,activation = \"relu\"))\n",
    "model.add(Dense(units=15,activation = \"relu\"))\n",
    "model.add(Dense(units=15,activation = \"relu\"))\n",
    "model.add(Dense(units=1,activation = \"sigmoid\"))\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\",optimizer = \"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 383 samples, validate on 165 samples\n",
      "Epoch 1/700\n",
      "383/383 [==============================] - 1s 3ms/sample - loss: 0.6923 - val_loss: 0.6945\n",
      "Epoch 2/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.6760 - val_loss: 0.6861\n",
      "Epoch 3/700\n",
      "383/383 [==============================] - 0s 164us/sample - loss: 0.6596 - val_loss: 0.6762\n",
      "Epoch 4/700\n",
      "383/383 [==============================] - 0s 168us/sample - loss: 0.6374 - val_loss: 0.6626\n",
      "Epoch 5/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 0.6106 - val_loss: 0.6456\n",
      "Epoch 6/700\n",
      "383/383 [==============================] - 0s 160us/sample - loss: 0.5777 - val_loss: 0.6213\n",
      "Epoch 7/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 0.5389 - val_loss: 0.5866\n",
      "Epoch 8/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.4965 - val_loss: 0.5541\n",
      "Epoch 9/700\n",
      "383/383 [==============================] - 0s 160us/sample - loss: 0.4501 - val_loss: 0.5200\n",
      "Epoch 10/700\n",
      "383/383 [==============================] - 0s 158us/sample - loss: 0.4018 - val_loss: 0.4762\n",
      "Epoch 11/700\n",
      "383/383 [==============================] - 0s 163us/sample - loss: 0.3583 - val_loss: 0.4505\n",
      "Epoch 12/700\n",
      "383/383 [==============================] - 0s 154us/sample - loss: 0.3175 - val_loss: 0.4149\n",
      "Epoch 13/700\n",
      "383/383 [==============================] - 0s 163us/sample - loss: 0.2827 - val_loss: 0.3888\n",
      "Epoch 14/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 0.2545 - val_loss: 0.3767\n",
      "Epoch 15/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.2357 - val_loss: 0.3640\n",
      "Epoch 16/700\n",
      "383/383 [==============================] - 0s 166us/sample - loss: 0.2139 - val_loss: 0.3422\n",
      "Epoch 17/700\n",
      "383/383 [==============================] - 0s 169us/sample - loss: 0.1983 - val_loss: 0.3295\n",
      "Epoch 18/700\n",
      "383/383 [==============================] - 0s 171us/sample - loss: 0.1874 - val_loss: 0.3244\n",
      "Epoch 19/700\n",
      "383/383 [==============================] - 0s 168us/sample - loss: 0.1766 - val_loss: 0.3309\n",
      "Epoch 20/700\n",
      "383/383 [==============================] - 0s 167us/sample - loss: 0.1691 - val_loss: 0.3007\n",
      "Epoch 21/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.1610 - val_loss: 0.3115\n",
      "Epoch 22/700\n",
      "383/383 [==============================] - 0s 153us/sample - loss: 0.1550 - val_loss: 0.2975\n",
      "Epoch 23/700\n",
      "383/383 [==============================] - 0s 153us/sample - loss: 0.1485 - val_loss: 0.2939\n",
      "Epoch 24/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 0.1438 - val_loss: 0.2876\n",
      "Epoch 25/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 0.1426 - val_loss: 0.2734\n",
      "Epoch 26/700\n",
      "383/383 [==============================] - 0s 150us/sample - loss: 0.1365 - val_loss: 0.2893\n",
      "Epoch 27/700\n",
      "383/383 [==============================] - 0s 160us/sample - loss: 0.1310 - val_loss: 0.2817\n",
      "Epoch 28/700\n",
      "383/383 [==============================] - 0s 163us/sample - loss: 0.1284 - val_loss: 0.2828\n",
      "Epoch 29/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.1308 - val_loss: 0.2697\n",
      "Epoch 30/700\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 78/700\n",
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     ]
    },
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     "text": [
      "Epoch 155/700\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
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     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
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     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 607/700\n",
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     ]
    },
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     "text": [
      "Epoch 681/700\n",
      "383/383 [==============================] - 0s 163us/sample - loss: 1.4584e-04 - val_loss: 0.5694\n",
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      "383/383 [==============================] - 0s 163us/sample - loss: 1.0883e-04 - val_loss: 0.5718\n",
      "Epoch 687/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 1.0966e-04 - val_loss: 0.5698\n",
      "Epoch 688/700\n",
      "383/383 [==============================] - 0s 158us/sample - loss: 1.1368e-04 - val_loss: 0.5715\n",
      "Epoch 689/700\n",
      "383/383 [==============================] - 0s 158us/sample - loss: 1.0230e-04 - val_loss: 0.5720\n",
      "Epoch 690/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 1.0225e-04 - val_loss: 0.5731\n",
      "Epoch 691/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 1.0531e-04 - val_loss: 0.5716\n",
      "Epoch 692/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 9.8575e-05 - val_loss: 0.5740\n",
      "Epoch 693/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 1.0657e-04 - val_loss: 0.5759\n",
      "Epoch 694/700\n",
      "383/383 [==============================] - 0s 155us/sample - loss: 9.6455e-05 - val_loss: 0.5713\n",
      "Epoch 695/700\n",
      "383/383 [==============================] - 0s 153us/sample - loss: 9.8804e-05 - val_loss: 0.5751\n",
      "Epoch 696/700\n",
      "383/383 [==============================] - 0s 159us/sample - loss: 9.8430e-05 - val_loss: 0.5764\n",
      "Epoch 697/700\n",
      "383/383 [==============================] - 0s 168us/sample - loss: 9.7357e-05 - val_loss: 0.5742\n",
      "Epoch 698/700\n",
      "383/383 [==============================] - 0s 164us/sample - loss: 9.3601e-05 - val_loss: 0.5753\n",
      "Epoch 699/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 1.0032e-04 - val_loss: 0.5758\n",
      "Epoch 700/700\n",
      "383/383 [==============================] - 0s 161us/sample - loss: 9.9725e-05 - val_loss: 0.5761\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fd2607a23d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=x_train, y=y_train, epochs=700,validation_data=(x_test,y_test),verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  0.5628963616953372,\n",
       "  0.5626216052337515,\n",
       "  0.5606451206296578,\n",
       "  0.5637491780898575,\n",
       "  0.5630299050879435,\n",
       "  0.5627159002322177,\n",
       "  0.5657733507432001,\n",
       "  0.5615012656707508,\n",
       "  0.5651381269718343,\n",
       "  0.567634593014583,\n",
       "  0.5620680108340047,\n",
       "  0.5670928732196192,\n",
       "  0.5641085339464293,\n",
       "  0.5666772435272494,\n",
       "  0.5675612448911976,\n",
       "  0.5653544689796607,\n",
       "  0.5693557660856257,\n",
       "  0.565431153786458,\n",
       "  0.5730764440699129,\n",
       "  0.5690241194268214,\n",
       "  0.573481186292118,\n",
       "  0.5717981559002883,\n",
       "  0.56983299455338,\n",
       "  0.5714563578509009,\n",
       "  0.5720420905271072,\n",
       "  0.5731009363367299,\n",
       "  0.5716266883270538,\n",
       "  0.5739733257222407,\n",
       "  0.5759250260821316,\n",
       "  0.5713229851404117,\n",
       "  0.5751344015334015,\n",
       "  0.576434902815429,\n",
       "  0.5742440939065806,\n",
       "  0.5753357098985649,\n",
       "  0.5758064350520267,\n",
       "  0.5761241587848591]}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelKaybi = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd280d67d50>"
      ]
     },
     "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": [
    "modelKaybi.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(units=30,activation = \"relu\"))\n",
    "model.add(Dense(units=15,activation = \"relu\"))\n",
    "model.add(Dense(units=15,activation = \"relu\"))\n",
    "model.add(Dense(units=1,activation = \"sigmoid\"))\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\",optimizer = \"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlyStopping = EarlyStopping(monitor=\"val_loss\",mode=\"min\",verbose=1,patience=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 383 samples, validate on 165 samples\n",
      "Epoch 1/700\n",
      "383/383 [==============================] - 1s 2ms/sample - loss: 0.6838 - val_loss: 0.6942\n",
      "Epoch 2/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.6635 - val_loss: 0.6854\n",
      "Epoch 3/700\n",
      "383/383 [==============================] - 0s 158us/sample - loss: 0.6418 - val_loss: 0.6724\n",
      "Epoch 4/700\n",
      "383/383 [==============================] - 0s 166us/sample - loss: 0.6175 - val_loss: 0.6551\n",
      "Epoch 5/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.5895 - val_loss: 0.6336\n",
      "Epoch 6/700\n",
      "383/383 [==============================] - 0s 160us/sample - loss: 0.5586 - val_loss: 0.6129\n",
      "Epoch 7/700\n",
      "383/383 [==============================] - 0s 157us/sample - loss: 0.5254 - val_loss: 0.5867\n",
      "Epoch 8/700\n",
      "383/383 [==============================] - 0s 154us/sample - loss: 0.4876 - val_loss: 0.5665\n",
      "Epoch 9/700\n",
      "383/383 [==============================] - 0s 154us/sample - loss: 0.4487 - val_loss: 0.5254\n",
      "Epoch 10/700\n",
      "383/383 [==============================] - 0s 158us/sample - loss: 0.4031 - val_loss: 0.4837\n",
      "Epoch 11/700\n",
      "383/383 [==============================] - 0s 159us/sample - loss: 0.3603 - val_loss: 0.4506\n",
      "Epoch 12/700\n",
      "383/383 [==============================] - 0s 159us/sample - loss: 0.3160 - val_loss: 0.4128\n",
      "Epoch 13/700\n",
      "383/383 [==============================] - 0s 165us/sample - loss: 0.2798 - val_loss: 0.3808\n",
      "Epoch 14/700\n",
      "383/383 [==============================] - 0s 160us/sample - loss: 0.2481 - val_loss: 0.3653\n",
      "Epoch 15/700\n",
      "383/383 [==============================] - 0s 165us/sample - loss: 0.2224 - val_loss: 0.3338\n",
      "Epoch 16/700\n",
      "383/383 [==============================] - 0s 163us/sample - loss: 0.2022 - val_loss: 0.3315\n",
      "Epoch 17/700\n",
      "383/383 [==============================] - 0s 159us/sample - loss: 0.1855 - val_loss: 0.3052\n",
      "Epoch 18/700\n",
      "383/383 [==============================] - 0s 154us/sample - loss: 0.1701 - val_loss: 0.3053\n",
      "Epoch 19/700\n",
      "383/383 [==============================] - 0s 156us/sample - loss: 0.1605 - val_loss: 0.2844\n",
      "Epoch 20/700\n",
      "383/383 [==============================] - 0s 158us/sample - loss: 0.1508 - val_loss: 0.2876\n",
      "Epoch 21/700\n",
      "383/383 [==============================] - 0s 157us/sample - loss: 0.1423 - val_loss: 0.2766\n",
      "Epoch 22/700\n",
      "383/383 [==============================] - 0s 168us/sample - loss: 0.1365 - val_loss: 0.2728\n",
      "Epoch 23/700\n",
      "383/383 [==============================] - 0s 159us/sample - loss: 0.1334 - val_loss: 0.2582\n",
      "Epoch 24/700\n",
      "383/383 [==============================] - 0s 167us/sample - loss: 0.1311 - val_loss: 0.2644\n",
      "Epoch 25/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.1201 - val_loss: 0.2519\n",
      "Epoch 26/700\n",
      "383/383 [==============================] - 0s 158us/sample - loss: 0.1161 - val_loss: 0.2508\n",
      "Epoch 27/700\n",
      "383/383 [==============================] - 0s 159us/sample - loss: 0.1118 - val_loss: 0.2596\n",
      "Epoch 28/700\n",
      "383/383 [==============================] - 0s 157us/sample - loss: 0.1087 - val_loss: 0.2445\n",
      "Epoch 29/700\n",
      "383/383 [==============================] - 0s 163us/sample - loss: 0.1087 - val_loss: 0.2539\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 78/700\n",
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     ]
    },
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     "text": [
      "Epoch 155/700\n",
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      "Epoch 197/700\n",
      "383/383 [==============================] - 0s 168us/sample - loss: 0.0174 - val_loss: 0.1385\n",
      "Epoch 198/700\n",
      "383/383 [==============================] - 0s 164us/sample - loss: 0.0186 - val_loss: 0.1416\n",
      "Epoch 199/700\n",
      "383/383 [==============================] - 0s 163us/sample - loss: 0.0153 - val_loss: 0.1397\n",
      "Epoch 200/700\n",
      "383/383 [==============================] - 0s 159us/sample - loss: 0.0153 - val_loss: 0.1393\n",
      "Epoch 201/700\n",
      "383/383 [==============================] - 0s 154us/sample - loss: 0.0155 - val_loss: 0.1415\n",
      "Epoch 202/700\n",
      "383/383 [==============================] - 0s 155us/sample - loss: 0.0165 - val_loss: 0.1429\n",
      "Epoch 203/700\n",
      "383/383 [==============================] - 0s 156us/sample - loss: 0.0145 - val_loss: 0.1412\n",
      "Epoch 204/700\n",
      "383/383 [==============================] - 0s 168us/sample - loss: 0.0153 - val_loss: 0.1446\n",
      "Epoch 205/700\n",
      "383/383 [==============================] - 0s 165us/sample - loss: 0.0157 - val_loss: 0.1382\n",
      "Epoch 206/700\n",
      "383/383 [==============================] - 0s 164us/sample - loss: 0.0149 - val_loss: 0.1413\n",
      "Epoch 207/700\n",
      "383/383 [==============================] - 0s 154us/sample - loss: 0.0175 - val_loss: 0.1543\n",
      "Epoch 208/700\n",
      "383/383 [==============================] - 0s 163us/sample - loss: 0.0162 - val_loss: 0.1380\n",
      "Epoch 209/700\n",
      "383/383 [==============================] - 0s 156us/sample - loss: 0.0137 - val_loss: 0.1409\n",
      "Epoch 210/700\n",
      "383/383 [==============================] - 0s 163us/sample - loss: 0.0140 - val_loss: 0.1455\n",
      "Epoch 211/700\n",
      "383/383 [==============================] - 0s 165us/sample - loss: 0.0158 - val_loss: 0.1393\n",
      "Epoch 212/700\n",
      "383/383 [==============================] - 0s 163us/sample - loss: 0.0131 - val_loss: 0.1500\n",
      "Epoch 213/700\n",
      "383/383 [==============================] - 0s 173us/sample - loss: 0.0138 - val_loss: 0.1353\n",
      "Epoch 214/700\n",
      "383/383 [==============================] - 0s 160us/sample - loss: 0.0127 - val_loss: 0.1378\n",
      "Epoch 215/700\n",
      "383/383 [==============================] - 0s 173us/sample - loss: 0.0135 - val_loss: 0.1386\n",
      "Epoch 216/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.0132 - val_loss: 0.1363\n",
      "Epoch 217/700\n",
      "383/383 [==============================] - 0s 152us/sample - loss: 0.0133 - val_loss: 0.1364\n",
      "Epoch 218/700\n",
      "383/383 [==============================] - 0s 159us/sample - loss: 0.0153 - val_loss: 0.1444\n",
      "Epoch 219/700\n",
      "383/383 [==============================] - 0s 164us/sample - loss: 0.0172 - val_loss: 0.1381\n",
      "Epoch 220/700\n",
      "383/383 [==============================] - 0s 160us/sample - loss: 0.0182 - val_loss: 0.1536\n",
      "Epoch 221/700\n",
      "383/383 [==============================] - 0s 173us/sample - loss: 0.0122 - val_loss: 0.1337\n",
      "Epoch 222/700\n",
      "383/383 [==============================] - 0s 170us/sample - loss: 0.0166 - val_loss: 0.1627\n",
      "Epoch 223/700\n",
      "383/383 [==============================] - 0s 158us/sample - loss: 0.0167 - val_loss: 0.1398\n",
      "Epoch 224/700\n",
      "383/383 [==============================] - 0s 157us/sample - loss: 0.0123 - val_loss: 0.1455\n",
      "Epoch 225/700\n",
      "383/383 [==============================] - 0s 160us/sample - loss: 0.0117 - val_loss: 0.1380\n",
      "Epoch 226/700\n",
      "383/383 [==============================] - 0s 166us/sample - loss: 0.0112 - val_loss: 0.1445\n",
      "Epoch 227/700\n",
      "383/383 [==============================] - 0s 154us/sample - loss: 0.0154 - val_loss: 0.1365\n",
      "Epoch 228/700\n",
      "383/383 [==============================] - 0s 163us/sample - loss: 0.0143 - val_loss: 0.1461\n",
      "Epoch 229/700\n",
      "383/383 [==============================] - 0s 167us/sample - loss: 0.0113 - val_loss: 0.1393\n",
      "Epoch 230/700\n",
      "383/383 [==============================] - 0s 165us/sample - loss: 0.0129 - val_loss: 0.1415\n",
      "Epoch 231/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "383/383 [==============================] - 0s 156us/sample - loss: 0.0127 - val_loss: 0.1426\n",
      "Epoch 232/700\n",
      "383/383 [==============================] - 0s 156us/sample - loss: 0.0108 - val_loss: 0.1428\n",
      "Epoch 233/700\n",
      "383/383 [==============================] - 0s 153us/sample - loss: 0.0110 - val_loss: 0.1441\n",
      "Epoch 234/700\n",
      "383/383 [==============================] - 0s 153us/sample - loss: 0.0151 - val_loss: 0.1369\n",
      "Epoch 235/700\n",
      "383/383 [==============================] - 0s 154us/sample - loss: 0.0121 - val_loss: 0.1399\n",
      "Epoch 236/700\n",
      "383/383 [==============================] - 0s 151us/sample - loss: 0.0106 - val_loss: 0.1449\n",
      "Epoch 237/700\n",
      "383/383 [==============================] - 0s 152us/sample - loss: 0.0102 - val_loss: 0.1408\n",
      "Epoch 238/700\n",
      "383/383 [==============================] - 0s 155us/sample - loss: 0.0120 - val_loss: 0.1422\n",
      "Epoch 239/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.0105 - val_loss: 0.1489\n",
      "Epoch 240/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.0090 - val_loss: 0.1369\n",
      "Epoch 241/700\n",
      "383/383 [==============================] - 0s 159us/sample - loss: 0.0109 - val_loss: 0.1433\n",
      "Epoch 242/700\n",
      "383/383 [==============================] - 0s 155us/sample - loss: 0.0107 - val_loss: 0.1536\n",
      "Epoch 243/700\n",
      "383/383 [==============================] - 0s 162us/sample - loss: 0.0110 - val_loss: 0.1514\n",
      "Epoch 244/700\n",
      "383/383 [==============================] - 0s 169us/sample - loss: 0.0107 - val_loss: 0.1428\n",
      "Epoch 245/700\n",
      "383/383 [==============================] - 0s 156us/sample - loss: 0.0096 - val_loss: 0.1440\n",
      "Epoch 246/700\n",
      "383/383 [==============================] - 0s 154us/sample - loss: 0.0095 - val_loss: 0.1471\n",
      "Epoch 00246: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fd2713130d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=x_train, y=y_train, epochs = 700, validation_data = (x_test,y_test), verbose = 1, callbacks=[earlyStopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelKaybi = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2909f64d0>"
      ]
     },
     "execution_count": 29,
     "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": [
    "modelKaybi.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(units=30,activation = \"relu\"))\n",
    "model.add(Dropout(0.6))\n",
    "\n",
    "model.add(Dense(units=15,activation = \"relu\"))\n",
    "model.add(Dropout(0.6))\n",
    "\n",
    "model.add(Dense(units=15,activation = \"relu\"))\n",
    "model.add(Dropout(0.6))\n",
    "\n",
    "model.add(Dense(units=1,activation = \"sigmoid\"))\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\",optimizer = \"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "Train on 383 samples, validate on 165 samples\n",
      "Epoch 1/700\n",
      "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "383/383 [==============================] - 1s 3ms/sample - loss: 0.7056 - val_loss: 0.7051\n",
      "Epoch 2/700\n",
      "383/383 [==============================] - 0s 177us/sample - loss: 0.7232 - val_loss: 0.7016\n",
      "Epoch 3/700\n",
      "383/383 [==============================] - 0s 176us/sample - loss: 0.6990 - val_loss: 0.6984\n",
      "Epoch 4/700\n",
      "383/383 [==============================] - 0s 179us/sample - loss: 0.6997 - val_loss: 0.6957\n",
      "Epoch 5/700\n",
      "383/383 [==============================] - 0s 177us/sample - loss: 0.6822 - val_loss: 0.6935\n",
      "Epoch 6/700\n",
      "383/383 [==============================] - 0s 166us/sample - loss: 0.6980 - val_loss: 0.6917\n",
      "Epoch 7/700\n",
      "383/383 [==============================] - 0s 176us/sample - loss: 0.6780 - val_loss: 0.6884\n",
      "Epoch 8/700\n",
      "383/383 [==============================] - 0s 173us/sample - loss: 0.6905 - val_loss: 0.6853\n",
      "Epoch 9/700\n",
      "383/383 [==============================] - 0s 173us/sample - loss: 0.6658 - val_loss: 0.6829\n",
      "Epoch 10/700\n",
      "383/383 [==============================] - 0s 173us/sample - loss: 0.6823 - val_loss: 0.6812\n",
      "Epoch 11/700\n",
      "383/383 [==============================] - 0s 186us/sample - loss: 0.6680 - val_loss: 0.6795\n",
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      "Epoch 16/700\n",
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      "Epoch 19/700\n",
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      "Epoch 20/700\n",
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      "Epoch 21/700\n",
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      "Epoch 22/700\n",
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      "Epoch 24/700\n",
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      "Epoch 25/700\n",
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      "Epoch 26/700\n",
      "383/383 [==============================] - 0s 183us/sample - loss: 0.6222 - val_loss: 0.6493\n",
      "Epoch 27/700\n",
      "383/383 [==============================] - 0s 181us/sample - loss: 0.6231 - val_loss: 0.6446\n",
      "Epoch 28/700\n",
      "383/383 [==============================] - 0s 178us/sample - loss: 0.6066 - val_loss: 0.6393\n",
      "Epoch 29/700\n",
      "383/383 [==============================] - 0s 175us/sample - loss: 0.6077 - val_loss: 0.6357\n",
      "Epoch 30/700\n",
      "383/383 [==============================] - 0s 176us/sample - loss: 0.5980 - val_loss: 0.6283\n",
      "Epoch 31/700\n",
      "383/383 [==============================] - 0s 171us/sample - loss: 0.6194 - val_loss: 0.6235\n",
      "Epoch 32/700\n",
      "383/383 [==============================] - 0s 179us/sample - loss: 0.6094 - val_loss: 0.6135\n",
      "Epoch 33/700\n",
      "383/383 [==============================] - 0s 174us/sample - loss: 0.5909 - val_loss: 0.6017\n",
      "Epoch 34/700\n",
      "383/383 [==============================] - 0s 174us/sample - loss: 0.5970 - val_loss: 0.5911\n",
      "Epoch 35/700\n",
      "383/383 [==============================] - 0s 173us/sample - loss: 0.5978 - val_loss: 0.5834\n",
      "Epoch 36/700\n",
      "383/383 [==============================] - 0s 168us/sample - loss: 0.5757 - val_loss: 0.5753\n",
      "Epoch 37/700\n",
      "383/383 [==============================] - 0s 179us/sample - loss: 0.5678 - val_loss: 0.5627\n",
      "Epoch 38/700\n",
      "383/383 [==============================] - 0s 172us/sample - loss: 0.5490 - val_loss: 0.5501\n",
      "Epoch 39/700\n",
      "383/383 [==============================] - 0s 175us/sample - loss: 0.5613 - val_loss: 0.5399\n",
      "Epoch 40/700\n",
      "383/383 [==============================] - 0s 178us/sample - loss: 0.5391 - val_loss: 0.5338\n",
      "Epoch 41/700\n",
      "383/383 [==============================] - 0s 174us/sample - loss: 0.5578 - val_loss: 0.5235\n",
      "Epoch 42/700\n",
      "383/383 [==============================] - 0s 172us/sample - loss: 0.5194 - val_loss: 0.5135\n",
      "Epoch 43/700\n",
      "383/383 [==============================] - 0s 172us/sample - loss: 0.5293 - val_loss: 0.5002\n",
      "Epoch 44/700\n",
      "383/383 [==============================] - 0s 172us/sample - loss: 0.4884 - val_loss: 0.4832\n",
      "Epoch 45/700\n",
      "383/383 [==============================] - 0s 173us/sample - loss: 0.5163 - val_loss: 0.4663\n",
      "Epoch 46/700\n",
      "383/383 [==============================] - 0s 180us/sample - loss: 0.5004 - val_loss: 0.4610\n",
      "Epoch 47/700\n",
      "383/383 [==============================] - 0s 174us/sample - loss: 0.4831 - val_loss: 0.4603\n",
      "Epoch 48/700\n",
      "383/383 [==============================] - 0s 176us/sample - loss: 0.4753 - val_loss: 0.4507\n",
      "Epoch 49/700\n",
      "383/383 [==============================] - 0s 177us/sample - loss: 0.4650 - val_loss: 0.4353\n",
      "Epoch 50/700\n",
      "383/383 [==============================] - 0s 179us/sample - loss: 0.4750 - val_loss: 0.4267\n",
      "Epoch 51/700\n",
      "383/383 [==============================] - 0s 179us/sample - loss: 0.4407 - val_loss: 0.4295\n",
      "Epoch 52/700\n",
      "383/383 [==============================] - 0s 185us/sample - loss: 0.4333 - val_loss: 0.4231\n",
      "Epoch 53/700\n",
      "383/383 [==============================] - 0s 178us/sample - loss: 0.4620 - val_loss: 0.4062\n",
      "Epoch 54/700\n",
      "383/383 [==============================] - 0s 173us/sample - loss: 0.4332 - val_loss: 0.3883\n",
      "Epoch 55/700\n",
      "383/383 [==============================] - 0s 171us/sample - loss: 0.4197 - val_loss: 0.3664\n",
      "Epoch 56/700\n",
      "383/383 [==============================] - 0s 178us/sample - loss: 0.4373 - val_loss: 0.3653\n",
      "Epoch 57/700\n",
      "383/383 [==============================] - 0s 180us/sample - loss: 0.3810 - val_loss: 0.3813\n",
      "Epoch 58/700\n",
      "383/383 [==============================] - 0s 170us/sample - loss: 0.3774 - val_loss: 0.3667\n",
      "Epoch 59/700\n",
      "383/383 [==============================] - 0s 179us/sample - loss: 0.3849 - val_loss: 0.3511\n",
      "Epoch 60/700\n",
      "383/383 [==============================] - 0s 175us/sample - loss: 0.3782 - val_loss: 0.3374\n",
      "Epoch 61/700\n",
      "383/383 [==============================] - 0s 179us/sample - loss: 0.3705 - val_loss: 0.3284\n",
      "Epoch 62/700\n",
      "383/383 [==============================] - 0s 171us/sample - loss: 0.3899 - val_loss: 0.3169\n",
      "Epoch 63/700\n",
      "383/383 [==============================] - 0s 172us/sample - loss: 0.3476 - val_loss: 0.3244\n",
      "Epoch 64/700\n",
      "383/383 [==============================] - 0s 169us/sample - loss: 0.3280 - val_loss: 0.3104\n",
      "Epoch 65/700\n",
      "383/383 [==============================] - 0s 176us/sample - loss: 0.3597 - val_loss: 0.2965\n",
      "Epoch 66/700\n",
      "383/383 [==============================] - 0s 176us/sample - loss: 0.3891 - val_loss: 0.3127\n",
      "Epoch 67/700\n",
      "383/383 [==============================] - 0s 176us/sample - loss: 0.3146 - val_loss: 0.3052\n",
      "Epoch 68/700\n",
      "383/383 [==============================] - 0s 176us/sample - loss: 0.3525 - val_loss: 0.2984\n",
      "Epoch 69/700\n",
      "383/383 [==============================] - 0s 174us/sample - loss: 0.3300 - val_loss: 0.3116\n",
      "Epoch 70/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "383/383 [==============================] - 0s 175us/sample - loss: 0.3009 - val_loss: 0.2996\n",
      "Epoch 71/700\n",
      "383/383 [==============================] - 0s 178us/sample - loss: 0.3051 - val_loss: 0.2916\n",
      "Epoch 72/700\n",
      "383/383 [==============================] - 0s 177us/sample - loss: 0.3463 - val_loss: 0.2958\n",
      "Epoch 73/700\n",
      "383/383 [==============================] - 0s 183us/sample - loss: 0.3288 - val_loss: 0.2879\n",
      "Epoch 74/700\n",
      "383/383 [==============================] - 0s 171us/sample - loss: 0.3045 - val_loss: 0.2962\n",
      "Epoch 75/700\n",
      "383/383 [==============================] - 0s 173us/sample - loss: 0.2879 - val_loss: 0.2863\n",
      "Epoch 76/700\n",
      "383/383 [==============================] - 0s 178us/sample - loss: 0.3419 - val_loss: 0.2744\n",
      "Epoch 77/700\n",
      "383/383 [==============================] - 0s 173us/sample - loss: 0.2926 - val_loss: 0.2623\n",
      "Epoch 78/700\n",
      "383/383 [==============================] - 0s 177us/sample - loss: 0.3009 - val_loss: 0.2859\n",
      "Epoch 79/700\n",
      "383/383 [==============================] - 0s 178us/sample - loss: 0.2856 - val_loss: 0.2709\n",
      "Epoch 80/700\n",
      "383/383 [==============================] - 0s 179us/sample - loss: 0.3264 - val_loss: 0.2686\n",
      "Epoch 81/700\n",
      "383/383 [==============================] - 0s 168us/sample - loss: 0.2863 - val_loss: 0.2587\n",
      "Epoch 82/700\n",
      "383/383 [==============================] - 0s 175us/sample - loss: 0.2449 - val_loss: 0.2695\n",
      "Epoch 83/700\n",
      "383/383 [==============================] - 0s 175us/sample - loss: 0.2740 - val_loss: 0.2719\n",
      "Epoch 84/700\n",
      "383/383 [==============================] - 0s 165us/sample - loss: 0.2693 - val_loss: 0.2871\n",
      "Epoch 85/700\n",
      "383/383 [==============================] - 0s 169us/sample - loss: 0.2462 - val_loss: 0.2983\n",
      "Epoch 86/700\n",
      "383/383 [==============================] - 0s 174us/sample - loss: 0.2731 - val_loss: 0.2645\n",
      "Epoch 87/700\n",
      "383/383 [==============================] - 0s 178us/sample - loss: 0.2568 - val_loss: 0.2605\n",
      "Epoch 88/700\n",
      "383/383 [==============================] - 0s 180us/sample - loss: 0.2777 - val_loss: 0.2726\n",
      "Epoch 89/700\n",
      "383/383 [==============================] - 0s 179us/sample - loss: 0.2368 - val_loss: 0.2575\n",
      "Epoch 90/700\n",
      "383/383 [==============================] - 0s 175us/sample - loss: 0.2746 - val_loss: 0.2522\n",
      "Epoch 91/700\n",
      "383/383 [==============================] - 0s 171us/sample - loss: 0.2603 - val_loss: 0.2597\n",
      "Epoch 92/700\n",
      "383/383 [==============================] - 0s 177us/sample - loss: 0.2616 - val_loss: 0.2633\n",
      "Epoch 93/700\n",
      "383/383 [==============================] - 0s 176us/sample - loss: 0.2353 - val_loss: 0.2708\n",
      "Epoch 94/700\n",
      "383/383 [==============================] - 0s 177us/sample - loss: 0.2718 - val_loss: 0.2686\n",
      "Epoch 95/700\n",
      "383/383 [==============================] - 0s 180us/sample - loss: 0.2726 - val_loss: 0.2731\n",
      "Epoch 96/700\n",
      "383/383 [==============================] - 0s 176us/sample - loss: 0.2213 - val_loss: 0.2693\n",
      "Epoch 97/700\n",
      "383/383 [==============================] - 0s 172us/sample - loss: 0.2191 - val_loss: 0.2688\n",
      "Epoch 98/700\n",
      "383/383 [==============================] - 0s 174us/sample - loss: 0.2038 - val_loss: 0.2746\n",
      "Epoch 99/700\n",
      "383/383 [==============================] - 0s 181us/sample - loss: 0.2133 - val_loss: 0.2742\n",
      "Epoch 100/700\n",
      "383/383 [==============================] - 0s 167us/sample - loss: 0.2321 - val_loss: 0.2685\n",
      "Epoch 101/700\n",
      "383/383 [==============================] - 0s 172us/sample - loss: 0.2450 - val_loss: 0.2681\n",
      "Epoch 102/700\n",
      "383/383 [==============================] - 0s 174us/sample - loss: 0.2100 - val_loss: 0.2745\n",
      "Epoch 103/700\n",
      "383/383 [==============================] - 0s 182us/sample - loss: 0.2177 - val_loss: 0.2729\n",
      "Epoch 104/700\n",
      "383/383 [==============================] - 0s 172us/sample - loss: 0.2058 - val_loss: 0.2723\n",
      "Epoch 105/700\n",
      "383/383 [==============================] - 0s 172us/sample - loss: 0.2416 - val_loss: 0.2709\n",
      "Epoch 106/700\n",
      "383/383 [==============================] - 0s 174us/sample - loss: 0.2290 - val_loss: 0.2684\n",
      "Epoch 107/700\n",
      "383/383 [==============================] - 0s 177us/sample - loss: 0.2148 - val_loss: 0.2677\n",
      "Epoch 108/700\n",
      "383/383 [==============================] - 0s 169us/sample - loss: 0.2207 - val_loss: 0.2713\n",
      "Epoch 109/700\n",
      "383/383 [==============================] - 0s 176us/sample - loss: 0.2113 - val_loss: 0.2694\n",
      "Epoch 110/700\n",
      "383/383 [==============================] - 0s 174us/sample - loss: 0.2061 - val_loss: 0.2691\n",
      "Epoch 111/700\n",
      "383/383 [==============================] - 0s 177us/sample - loss: 0.2337 - val_loss: 0.2754\n",
      "Epoch 112/700\n",
      "383/383 [==============================] - 0s 179us/sample - loss: 0.2089 - val_loss: 0.2765\n",
      "Epoch 113/700\n",
      "383/383 [==============================] - 0s 181us/sample - loss: 0.1997 - val_loss: 0.2571\n",
      "Epoch 114/700\n",
      "383/383 [==============================] - 0s 178us/sample - loss: 0.2258 - val_loss: 0.2562\n",
      "Epoch 115/700\n",
      "383/383 [==============================] - 0s 179us/sample - loss: 0.1946 - val_loss: 0.2644\n",
      "Epoch 00115: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fd25528a050>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=x_train, y=y_train, epochs = 700, validation_data = (x_test,y_test), verbose = 1, callbacks=[earlyStopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "kayipDf = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2a0c56d90>"
      ]
     },
     "execution_count": 33,
     "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": [
    "kayipDf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "tahminlerimiz = model.predict_classes(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [0]], dtype=int32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tahminlerimiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.93      0.92        91\n",
      "           1       0.92      0.88      0.90        74\n",
      "\n",
      "    accuracy                           0.91       165\n",
      "   macro avg       0.91      0.91      0.91       165\n",
      "weighted avg       0.91      0.91      0.91       165\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,tahminlerimiz))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[85  6]\n",
      " [ 9 65]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,tahminlerimiz))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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