Deep Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import keras\n",
    "\n",
    "np.random.seed(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration"
   ]
  },
  {
   "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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "data['normalizedAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1,1))\n",
    "data = data.drop(['Amount'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V22</th>\n",
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       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.342475</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>1.160686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>0.140534</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.073403</td>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Class  normalizedAmount  \n",
       "0 -0.189115  0.133558 -0.021053      0          0.244964  \n",
       "1  0.125895 -0.008983  0.014724      0         -0.342475  \n",
       "2 -0.139097 -0.055353 -0.059752      0          1.160686  \n",
       "3 -0.221929  0.062723  0.061458      0          0.140534  \n",
       "4  0.502292  0.219422  0.215153      0         -0.073403  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "      <th>normalizedAmount</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "      <td>0.244964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.342475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>1.160686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>0.140534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.073403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  0.090794  ... -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Class  normalizedAmount  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053      0          0.244964  \n",
       "1  0.167170  0.125895 -0.008983  0.014724      0         -0.342475  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752      0          1.160686  \n",
       "3  0.647376 -0.221929  0.062723  0.061458      0          0.140534  \n",
       "4 -0.206010  0.502292  0.219422  0.215153      0         -0.073403  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['Time'],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, data.columns != 'Class']\n",
    "y = data.iloc[:, data.columns == 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class\n",
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199364, 29)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85443, 29)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array(X_train)\n",
    "X_test = np.array(X_test)\n",
    "y_train = np.array(y_train)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(units=16, input_dim = 29,activation='relu'),\n",
    "    Dense(units=24,activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(20,activation='relu'),\n",
    "    Dense(24,activation='relu'),\n",
    "    Dense(1,activation='sigmoid'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 16)                480       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 24)                408       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 24)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 20)                500       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 24)                504       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 25        \n",
      "=================================================================\n",
      "Total params: 1,917\n",
      "Trainable params: 1,917\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "13291/13291 [==============================] - 34s 1ms/step - loss: 0.0297 - accuracy: 0.9899 0s - loss: 0.0301 - \n",
      "Epoch 2/5\n",
      "13291/13291 [==============================] - 17s 1ms/step - loss: 0.0041 - accuracy: 0.9992\n",
      "Epoch 3/5\n",
      "13291/13291 [==============================] - 18s 1ms/step - loss: 0.0038 - accuracy: 0.9993\n",
      "Epoch 4/5\n",
      "13291/13291 [==============================] - 17s 1ms/step - loss: 0.0033 - accuracy: 0.9994\n",
      "Epoch 5/5\n",
      "13291/13291 [==============================] - 17s 1ms/step - loss: 0.0035 - accuracy: 0.9994\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x26cdda95d90>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "model.fit(X_train,y_train,batch_size=15,epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2671/2671 [==============================] - 3s 923us/step - loss: 0.0036 - accuracy: 0.9994\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0036042602732777596, 0.9994148015975952]\n"
     ]
    }
   ],
   "source": [
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_test = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(y_test, y_pred.round())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[85277    19]\n",
      " [   31   116]]\n"
     ]
    }
   ],
   "source": [
    "print(cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[85277    19]\n",
      " [   31   116]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cnf_matrix, classes=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}