Deep-Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/Users/rodrigobeceiro/anaconda/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "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": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "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  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<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>Class</th>\n",
       "      <th>normalizedAmount</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>0</td>\n",
       "      <td>0.244964</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>0</td>\n",
       "      <td>-0.342475</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>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",
       "    </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>0</td>\n",
       "      <td>-0.073403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "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  \\\n",
       "0  0.098698  0.363787        ...        -0.018307  0.277838 -0.110474   \n",
       "1  0.085102 -0.255425        ...        -0.225775 -0.638672  0.101288   \n",
       "2  0.247676 -1.514654        ...         0.247998  0.771679  0.909412   \n",
       "3  0.377436 -1.387024        ...        -0.108300  0.005274 -0.190321   \n",
       "4 -0.270533  0.817739        ...        -0.009431  0.798278 -0.137458   \n",
       "\n",
       "        V24       V25       V26       V27       V28  Class  normalizedAmount  \n",
       "0  0.066928  0.128539 -0.189115  0.133558 -0.021053      0          0.244964  \n",
       "1 -0.339846  0.167170  0.125895 -0.008983  0.014724      0         -0.342475  \n",
       "2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0          1.160686  \n",
       "3 -1.175575  0.647376 -0.221929  0.062723  0.061458      0          0.140534  \n",
       "4  0.141267 -0.206010  0.502292  0.219422  0.215153      0         -0.073403  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</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>Class</th>\n",
       "      <th>normalizedAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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  \\\n",
       "0  0.098698  0.363787  0.090794        ...        -0.018307  0.277838   \n",
       "1  0.085102 -0.255425 -0.166974        ...        -0.225775 -0.638672   \n",
       "2  0.247676 -1.514654  0.207643        ...         0.247998  0.771679   \n",
       "3  0.377436 -1.387024 -0.054952        ...        -0.108300  0.005274   \n",
       "4 -0.270533  0.817739  0.753074        ...        -0.009431  0.798278   \n",
       "\n",
       "        V23       V24       V25       V26       V27       V28  Class  \\\n",
       "0 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0   \n",
       "1  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0   \n",
       "2  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0   \n",
       "3 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0   \n",
       "4 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0   \n",
       "\n",
       "   normalizedAmount  \n",
       "0          0.244964  \n",
       "1         -0.342475  \n",
       "2          1.160686  \n",
       "3          0.140534  \n",
       "4         -0.073403  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['Time'],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = data.iloc[:, data.columns != 'Class']\n",
    "y = data.iloc[:, data.columns == 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199364, 29)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85443, 29)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "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": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 16)                480       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 24)                408       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 24)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 20)                500       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 24)                504       \n",
      "_________________________________________________________________\n",
      "dense_5 (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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "199364/199364 [==============================] - 39s 194us/step - loss: 0.0100 - acc: 0.9979\n",
      "Epoch 2/5\n",
      "199364/199364 [==============================] - 36s 181us/step - loss: 0.0040 - acc: 0.9993\n",
      "Epoch 3/5\n",
      "199364/199364 [==============================] - 31s 154us/step - loss: 0.0036 - acc: 0.9994\n",
      "Epoch 4/5\n",
      "199364/199364 [==============================] - 28s 140us/step - loss: 0.0034 - acc: 0.9994\n",
      "Epoch 5/5\n",
      "199364/199364 [==============================] - 30s 153us/step - loss: 0.0032 - acc: 0.9994\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1245ffbe0>"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85443/85443 [==============================] - 4s 46us/step\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.004388740259373431, 0.999403110845827]\n"
     ]
    }
   ],
   "source": [
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_test = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(y_test, y_pred.round())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[85281    15]\n",
      " [   36   111]]\n"
     ]
    }
   ],
   "source": [
    "print(cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[85281    15]\n",
      " [   36   111]]\n"
     ]
    }
   ],
   "source": [
    "plot_confusion_matrix(cnf_matrix, classes=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAEYCAYAAADRWAT6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWREOkmZmRfw/+4zKQVWcHzsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}