credit-card-fraud-detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Like & Upvote if you like it.\n",
    "\n",
    "# Credit Card Fraud Detection\n",
    "# General Neural Network\n",
    "# UnderSample Dataset\n",
    "# OverSample Dataset/SMOTE\n",
    "# Confusion Metrics\n",
    "# Data Visualiation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/creditcardfraud/creditcard.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load & Explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "df_cc = pd.read_csv(\"/kaggle/input/creditcardfraud/creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "\n",
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "            Time         V1         V2        V3        V4        V5  \\\n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
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       "\n",
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       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[5 rows x 31 columns]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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    "df_cc.tail()"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df_cc.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      False\n",
       "V1        False\n",
       "V2        False\n",
       "V3        False\n",
       "V4        False\n",
       "V5        False\n",
       "V6        False\n",
       "V7        False\n",
       "V8        False\n",
       "V9        False\n",
       "V10       False\n",
       "V11       False\n",
       "V12       False\n",
       "V13       False\n",
       "V14       False\n",
       "V15       False\n",
       "V16       False\n",
       "V17       False\n",
       "V18       False\n",
       "V19       False\n",
       "V20       False\n",
       "V21       False\n",
       "V22       False\n",
       "V23       False\n",
       "V24       False\n",
       "V25       False\n",
       "V26       False\n",
       "V27       False\n",
       "V28       False\n",
       "Amount    False\n",
       "Class     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         149.62\n",
       "1           2.69\n",
       "2         378.66\n",
       "3         123.50\n",
       "4          69.99\n",
       "           ...  \n",
       "284802      0.77\n",
       "284803     24.79\n",
       "284804     67.88\n",
       "284805     10.00\n",
       "284806    217.00\n",
       "Name: Amount, Length: 284807, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc.Amount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "df_cc[\"normalized_amount\"] = sc.fit_transform(df_cc[\"Amount\"].values.reshape(-1, 1))\n",
    "df_cc.drop(columns=[\"Amount\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.244964\n",
       "1   -0.342475\n",
       "2    1.160686\n",
       "3    0.140534\n",
       "4   -0.073403\n",
       "5   -0.338556\n",
       "6   -0.333279\n",
       "7   -0.190107\n",
       "8    0.019392\n",
       "9   -0.338516\n",
       "Name: normalized_amount, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc.normalized_amount.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop time column\n",
    "df_cc.drop(columns=[\"Time\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>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>normalized_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>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>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",
       "      <td>1</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>-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",
       "      <td>2</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>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",
       "      <td>3</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>-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",
       "      <td>4</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>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  normalized_amount  \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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_cc.iloc[:, df_cc.columns != \"Class\"]\n",
    "y = df_cc.iloc[:, df_cc.columns == \"Class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>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>V20</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>normalized_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>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>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251412</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>0.244964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</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>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069083</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>-0.342475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</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>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524980</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>1.160686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</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>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208038</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>0.140534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</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>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408542</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>-0.073403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 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  ...       V20       V21       V22       V23  \\\n",
       "0  0.098698  0.363787  0.090794  ...  0.251412 -0.018307  0.277838 -0.110474   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.069083 -0.225775 -0.638672  0.101288   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.524980  0.247998  0.771679  0.909412   \n",
       "3  0.377436 -1.387024 -0.054952  ... -0.208038 -0.108300  0.005274 -0.190321   \n",
       "4 -0.270533  0.817739  0.753074  ...  0.408542 -0.009431  0.798278 -0.137458   \n",
       "\n",
       "        V24       V25       V26       V27       V28  normalized_amount  \n",
       "0  0.066928  0.128539 -0.189115  0.133558 -0.021053           0.244964  \n",
       "1 -0.339846  0.167170  0.125895 -0.008983  0.014724          -0.342475  \n",
       "2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752           1.160686  \n",
       "3 -1.175575  0.647376 -0.221929  0.062723  0.061458           0.140534  \n",
       "4  0.141267 -0.206010  0.502292  0.219422  0.215153          -0.073403  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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=0.4, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(170884, 29)\n",
      "(113923, 29)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.41259991e+00, -1.12648053e+00,  7.69823445e-01, ...,\n",
       "         5.10523722e-02,  2.87660518e-02, -1.88748127e-01],\n",
       "       [-1.95457426e-01,  1.20343665e+00,  9.30156833e-02, ...,\n",
       "         4.35603879e-01,  2.34195684e-01, -3.33398885e-01],\n",
       "       [ 1.48983838e+00, -1.87834402e+00, -8.18912342e-01, ...,\n",
       "        -1.38744057e-02,  1.00683450e-02,  9.06167758e-01],\n",
       "       ...,\n",
       "       [ 1.51260229e+00, -9.49435315e-01, -2.19061626e-01, ...,\n",
       "        -1.41537086e-03,  3.66494418e-03, -2.13696185e-01],\n",
       "       [ 1.79886333e+00, -1.69979073e+00, -1.42182189e-01, ...,\n",
       "        -3.36155803e-02, -3.24705125e-02,  3.31682753e-01],\n",
       "       [-5.89399721e-01,  7.47828393e-01,  1.78478144e+00, ...,\n",
       "         8.65917860e-02,  1.18083774e-01, -2.89299995e-01]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 29)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = Sequential([\n",
    "    Dense(activation=\"relu\", units=16, input_dim=29)\n",
    "    ,Dense(activation=\"relu\", units=24)\n",
    "    ,Dropout(0.5)\n",
    "    ,Dense(activation=\"relu\", units=20)\n",
    "    ,Dense(activation=\"relu\", units=24)\n",
    "    ,Dense(activation=\"sigmoid\", units=1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\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": [
    "clf.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "170884/170884 [==============================] - 27s 158us/step - loss: 0.0101 - accuracy: 0.9979\n",
      "Epoch 2/5\n",
      "170884/170884 [==============================] - 26s 154us/step - loss: 0.0040 - accuracy: 0.9993\n",
      "Epoch 3/5\n",
      "170884/170884 [==============================] - 28s 164us/step - loss: 0.0040 - accuracy: 0.9992\n",
      "Epoch 4/5\n",
      "170884/170884 [==============================] - 26s 152us/step - loss: 0.0034 - accuracy: 0.9994\n",
      "Epoch 5/5\n",
      "170884/170884 [==============================] - 26s 151us/step - loss: 0.0033 - accuracy: 0.9994\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f6d515ed588>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
    "clf.fit(X_train, y_train, batch_size=15, nb_epoch=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113923/113923 [==============================] - 3s 29us/step\n"
     ]
    }
   ],
   "source": [
    "score = clf.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.004713199325703165, 0.9993240833282471]\n"
     ]
    }
   ],
   "source": [
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = clf.predict(X_test)\n",
    "y_test = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[113711,     13],\n",
       "       [    64,    135]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_predict.round())\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d1c2c8278>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# When we are dealing with critical cases like fraud, medical and other sensitive information. We have to \n",
    "# minimise our false Negative. It mean in our case it can be seen that 39 frauds are there which we said\n",
    "# its not but in actual they are. So 39 times people can fraud with us, which is a very bad thing.\n",
    "# So our main target would be reducing it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d241c8668>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 for entire database\n",
    "y_predict_all = clf.predict(X)\n",
    "y_expected = pd.DataFrame(y)\n",
    "cm_all = confusion_matrix(y_expected, y_predict_all.round())\n",
    "sns.heatmap(cm_all, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So for complete dataset, according to our model 94 times user can make fraud. So we have to improve our model\n",
    "# to reduce the possibilty of fraud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# before modell tuning, lets compare or model with random forst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rf, X_test_rf, y_train_rf, y_test_rf = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d1c0dff98>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD8CAYAAAC8TPVwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGTdJREFUeJzt3XmcFdWZ//HP0w3NkkCzDgMNLjFEBn6ZEEUkcWMTQaPgOqARopg2RhKNjuNCEhNDYswvBDWiv0FAFo0t4M+ACCIqRlG2jnRUEIYeEqWBQbAB1wB97zN/dDW5Qi+3obtvn/L79nVe3nvqVNW5r5CHx6dOVZm7IyIiYcjK9ARERCR9CtoiIgFR0BYRCYiCtohIQBS0RUQCoqAtIhIQBW0RkYAoaIuIBERBW0QkIE3q+wQHdm3WLZdymBZdzsj0FKQRKtu/1Y72GLWJOU07fOmoz9fQlGmLiASk3jNtEZEGlUxkegb1SkFbROIlUZbpGdQrBW0RiRX3ZKanUK8UtEUkXpIK2iIi4VCmLSISEF2IFBEJiDJtEZFwuFaPiIgERBciRUQCovKIiEhAdCFSRCQgyrRFRAKiC5EiIgHRhUgRkXC4q6YtIhIO1bRFRAKi8oiISECUaYuIBCRxINMzqFcK2iISLyqPiIgEJOblEb2NXUTiJZlMv1XDzE40s6KU9oGZ3WhmPzOzrSn956bsc7uZFZvZRjM7J6V/aNRXbGa3pfQfb2arzGyTmT1hZjk1/TwFbRGJlzoK2u6+0d17u3tv4GTgE+CpaPOkim3uvgjAzHoCI4FewFDgQTPLNrNsYDIwDOgJjIrGAtwTHas7sBsYW9PPU9AWkVjxxIG0Wy0MAv7b3d+pZsxwoMDd97n7X4FioG/Uit19s7vvBwqA4WZmwEBgXrT/TGBETRNR0BaRePFk+i19I4HHU76PM7M3zGy6mbWN+vKALSljSqK+qvrbA3vcveyQ/mopaItIvNSiPGJm+WZWmNLyDz1cVGe+AJgbdT0EnAD0BrYDEyuGVjIbP4L+amn1iIjESy0yaHefAkypYdgw4HV33xHts6Nig5k9DCyMvpYA3VL26wpsiz5X1r8LaGNmTaJsO3V8lZRpi0i81NGFyBSjSCmNmFnnlG0XAm9FnxcAI82smZkdD3QHVgNrgO7RSpEcykstC9zdgWXAJdH+Y4D5NU1GmbaIxEsdrtM2s5bA2cC1Kd2/MbPelJcy/laxzd3XmdkcYD1QBlzv0SMHzWwcsATIBqa7+7roWLcCBWY2AVgLTKtxTuXBvv4c2LW5fk8gQWrR5YxMT0EaobL9Wyur89bKp8/cm3bMaXHejUd9voamTFtE4iXmd0QqaItIvOjZIyIiAVGmLSISEGXaIiIBUaYtIhKQsrKaxwRMQVtE4qWelzFnmoK2iMSLatoiIgFR0BYRCYguRIqIBCSRyPQM6pWCtojEi8ojIiIBUdAWEQmIatoiIuHwpNZpi4iEQ+UREZGAaPWIiEhAlGl/fs0qeIonn34WM6P7Cccx4Y6baNYs5+D2Pz6zlIkPTuWfOnQAYNTF53PJBUOP6px7P/iQm39yN9v+Zwdd/rkTE39xO7mtWx3c/ubbG7ki/yZ+e9dtDBmgV3aFrvi/VvLhRx+RSCQpKyuj3zfOzfSUwhfzoK23sVdhx85dPDZvPk9Mv58/Pvr/SCaTLH7+T4eNGzrwLJ6cOZknZ06uVcBe/fobjJ8w8bD+qbPn0K9PbxY9MY1+fXoz7dE5B7clEgkmPfgIp/U96ch+lDRKg8++lD6nDFHArivu6bcA1Ri0zayHmd1qZveb2X3R539piMllWlkiwb59+ykrS/Dp3/fRsUO7tPed/tg8/m3sD7lw9HU8MHV22vste2UFw4cNBmD4sMG8+PKKg9v+MG8BZ/c/jXZt26T/I0Q+b5LJ9FuAqg3aZnYrUAAYsBpYE31+3Mxuq//pZU6njh34zqiLGXzRaAYMv5xWX2jJaaeefNi4pX9azoWjr+NH4yewfcdOAF5d9WfeLdlKwdT7eHLGZNZvLKaw6M20zvv+7j0H/3Lo2KEdpXv2AuWZ/wsvv8ZlI5SNxYm7s3jR46xauZhrxl6R6enEQ9LTbwGqqaY9Fujl7gdSO83sd8A64Nf1NbFM2/vBhyx7ZSVL5j5Cq1Zf5OYf/4qnl7zI+ecMPDim/+mncu7ZZ5GTk8MTTz3D+AkTmf77X/Pamtd5bfXrXPKdcQB88umnvLNlG316f5VR372R/fsP8Mmnn7L3gw+5eMz1ANz0/asr/Uuhwj33/Sc/uu5qsrOz6/eHS4M6s/8Itm/fQceO7Xl2cQEbNxbzyvJVmZ5W2D7nq0eSQBfgnUP6O0fbKmVm+UA+wIMTJ3DN6FFHM8eMWFlYRF6XTgdLEYPO+iZFb67/TNBuk9v64OdLLhjKpIeml39xuObKf6s0K3784XuB8pr2/EVL+eWPb/7M9vZt27BzVykdO7Rj565S2rXJBWDdhk3ccmf535G7937AKyvWkJ2dzaAzv1l3P1oa3PbtOwDYufN95s9fzCmn9FbQPkoeaNkjXTUF7RuBF8xsE7Al6jsG+DIwrqqd3H0KMAXgwK7NQf43SOdOHXnjrQ18+ve/07xZM1YVFtGrR/fPjKkIrgDLlq/kS8d2A+CbfU/igamz+daQAbRs2YIdO3fRpEkT2qdRi+5/ej/mL36ea668jPmLn2fAGd8AYMm8GQfHjJ8wkbNO66uAHbiWLVuQlZXFRx99TMuWLTh78FlM+OWkTE8rfIGWPdJVbdB292fN7CtAXyCP8np2CbDG3WP93yD/2qsHZw84ncuu+gHZ2dn0+MoJXDp8GA88PItePb7CgDP68ejc+by0fCXZTbLJbdWKCVHWfNqpJ7P5nS1cce1NALRs0Zy7f3pLWkH7misv4+af/Ir/v3AJnTt15HcTxtfr75TM6dSpI/PmTgOgSZNsCgr+yJLnXsrspOIg5s8eMa/nZS+hZtpSv1p00RpzOVzZ/q12tMf4+K4r0o45X/jpY0d9voamm2tEJF7KYl0EUNAWkZiJeXlEd0SKSLzU4TptM2tjZvPMbIOZvW1m3zCzdma21Mw2Rf9uG4216CbEYjN7w8xOSjnOmGj8JjMbk9J/spm9Ge1zv5nVWK5R0BaRWPFkMu2WhvuAZ929B/A14G3gNuAFd+8OvBB9BxgGdI9aPvAQgJm1A+4ETqV8UcedFYE+GpOfsl+Nz8JQ0BaReKmjTNvMWgNnAtMA3H2/u+8BhgMzo2EzgRHR5+HALC+3EmhjZp2Bc4Cl7l7q7ruBpcDQaFtrd1/h5StCZqUcq0oK2iISL3VXHvkSsBN4xMzWmtlUM/sC0MndtwNE//6naHwe/7ifBcqXR+fV0F9SSX+1FLRFJF4SibSbmeWbWWFKy085UhPgJOAhd/868DH/KIVUprJ6tB9Bf7W0ekREYqU274hMvXu7EiVAibtXPFdgHuVBe4eZdXb37VGJ472U8d1S9u8KbIv6+x/S/1LU37WS8dVSpi0i8VJH5RF3/x9gi5mdGHUNAtYDC4CKFSBjgPnR5wXA6GgVST9gb1Q+WQIMMbO20QXIIcCSaNuHZtYvWjUyOuVYVVKmLSLxUrcPjPoB8JiZ5QCbgasoT3bnmNlY4F3g0mjsIuBcoBj4JBqLu5ea2S8of7Q1wF3uXhp9vg6YAbQAFketWgraIhIvdfjAKHcvAvpUsmlQJWMduL6K40wHplfSXwj8n9rMSUFbROLl8/yUPxGR0Hgi3rexK2iLSLwo0xYRCUdtlvyFSEFbROJFQVtEJCDxLmkraItIvHhZvKO2graIxEu8Y7aCtojEiy5EioiERJm2iEg4lGmLiIREmbaISDi8LNMzqF8K2iISK65MW0QkIAraIiLhUKYtIhIQBW0RkYB4orKXnMeHgraIxIoybRGRgHhSmbaISDCUaYuIBMRdmbaISDCUaYuIBCSp1SMiIuHQhUgRkYAoaIuIBMTj/ThtBW0RiRdl2iIiAYn7kr+sTE9ARKQuJRKWdkuHmWWb2VozWxh9n2FmfzWzoqj1jvrNzO43s2Ize8PMTko5xhgz2xS1MSn9J5vZm9E+95tZjZNS0BaRWHG3tFuabgDePqTvFnfvHbWiqG8Y0D1q+cBDAGbWDrgTOBXoC9xpZm2jfR6KxlbsN7SmyShoi0iseNLSbjUxs67AecDUNE49HJjl5VYCbcysM3AOsNTdS919N7AUGBpta+3uK9zdgVnAiJpOoqAtIrHinn5Lw73Af3D4+3B+GZVAJplZs6gvD9iSMqYk6quuv6SS/mopaItIrNQm0zazfDMrTGn5Fccxs28B77n7nw85xe1AD+AUoB1wa8UulU3nCPqrpdUjIhIriWT6uai7TwGmVLH5NOACMzsXaA60NrNH3f3b0fZ9ZvYI8O/R9xKgW8r+XYFtUX//Q/pfivq7VjK+Wsq0RSRW6qo84u63u3tXdz8OGAm86O7fjmrRRCs9RgBvRbssAEZHq0j6AXvdfTuwBBhiZm2jC5BDgCXRtg/NrF90rNHA/Jp+nzJtEYmVZP2v037MzDpSXt4oAr4X9S8CzgWKgU+AqwDcvdTMfgGsicbd5e6l0efrgBlAC2Bx1KplXs/3fB7YtTnmN5XKkWjR5YxMT0EaobL9W4864q49ZnjaMefr784P7k4cZdoiEit69shRUkYlIg2pAcojGaVMW0RipTarR0KkoC0isRLz6oiCtojEi8ojIiIBifujWRW0RSRWYv4ydgVtEYkXr/SRHvGhoC0isVKm8oiISDiUaYuIBEQ1bRGRgCjTFhEJiDJtEZGAJJRpi4iEI4339QZNQVtEYiWpTFtEJBx6YJSISEB0IVJEJCBJU3lERCQYiUxPoJ4paItIrGj1iIhIQLR6REQkIFo9IiISEJVHREQCoiV/IiIBSSjTFhEJhzJtEZGAKGiLiAQk5q+IVNAWkXiJe6adlekJiIjUpUQtWnXMrLmZrTazv5jZOjP7edR/vJmtMrNNZvaEmeVE/c2i78XR9uNSjnV71L/RzM5J6R8a9RWb2W3p/D4FbRGJlaSl32qwDxjo7l8DegNDzawfcA8wyd27A7uBsdH4scBud/8yMCkah5n1BEYCvYChwINmlm1m2cBkYBjQExgVja2WgraIxEqyFq06Xu6j6GvTqDkwEJgX9c8ERkSfh0ffibYPMjOL+gvcfZ+7/xUoBvpGrdjdN7v7fqAgGlstBW0RiZXaBG0zyzezwpSWn3qsKCMuAt4DlgL/Dexx97JoSAmQF33OA7YARNv3Au1T+w/Zp6r+aulCpIjESm2ePeLuU4Ap1WxPAL3NrA3wFPAv1ZyysoKLV9NfWdJc4/QVtEUkVurj2SPuvsfMXgL6AW3MrEmUTXcFtkXDSoBuQImZNQFygdKU/gqp+1TVXyWVR0QkVupw9UjHKMPGzFoAg4G3gWXAJdGwMcD86POC6DvR9hfd3aP+kdHqkuOB7sBqYA3QPVqNkkP5xcoFNf0+ZdoiEivJuns4a2dgZrTKIwuY4+4LzWw9UGBmE4C1wLRo/DRgtpkVU55hjwRw93VmNgdYD5QB10dlF8xsHLAEyAamu/u6miZl5X8R1J8mOXlxf7ytiNSRsv1bj7q48Ytjr0g75vzknceCu39SmbaIxErcs0QFbRGJlbjfxq6gLSKxUmbxzrUVtEUkVuIdshW0RSRmVB4REQlIHS75a5QUtEUkVuIdshW0RSRmVB4REQlIIua5toK2iMSKMm0RkYC4Mm0RkXDEPdPWo1nrSbNmzVjx6kL+XLiUvxS9yJ0/vRmAgQNOZ/WqZylc8xx/WvYUJ5xwXGYnKrX28JSJbCv5C0VrX6h0+4knnsDylxfw8YebuelH19bJOXNycvjDYw+xYf1yXlv+NMce2xWAwYPOYNXKxax9/XlWrVzMgP6n1cn5QpbE024hUtCuJ/v27WPwkMs4uc/ZnNxnCOcM6c+pfU/igQfuZvSYcfQ5ZQiPF/yRO26/IdNTlVqaNWsO533riiq3l5bu4cYf/YTfTfrPWh/72GO78sLSuYf1X33VKHbv3kuPnqdz7/0Pc/evxgOw6/1SRlz4Hb5+0mCuHnsjMx65r9bnjBuvRQuRgnY9+vjjTwBo2rQJTZo2xd1xd1q3agVAbm4rtm/fkckpyhF4ZfkqSnfvqXL7zp3vU/jnv3DgwIHDtl1++UWseHUhhWue48HJ95CVld7/BS84fwizZ5cH8yeffIaBA04HoKho3cE/Q+vWbaR58+bk5OTU9ifFShmedgvREQdtM7uqLicSR1lZWRSueY7tW9/ghRdeZvWatVx77b/z9ILZ/G1zIVdccTH3/OaBTE9TGkiPHl/msksv4IyzRtDnlCEkEgkuv/yitPbtkvfPbCkpfxNVIpFg794PaN++7WfGXHTReRQVvcX+/fvrfO4h8Vr8E6KjuRD5c+CRyjZEbzTOB7DsXLKyvnAUpwlXMpmkzylDyM1tzZNzp9Gr14nccMN3Of+CK1m9Zi033/Q9fvt/7+Ta792S6alKAxg44HRO+vpXWbliEQAtWjRn585dAMybO5XjjjuGnJymHNMtj8I1zwHw+99PZeasOZgd/qz+1PeX9Oz5Fe7+5R0MO+/y+v8hjVzcL0RWG7TN7I2qNgGdqtov9Q3HenMN7N37AX96+TWGnjOAf/1qT1avWQvAnLkLeGbhYxmenTQUM2P2o3MZ/+NfH7btkkuvAcpr2tOnTmLQ2Zd+ZvvWku1069qFrVu3k52dTW5ua0pLdwOQl9eZeXOncdXVN7B58zv1/0MauVAz6HTVVB7pBIwGzq+kvV+/Uwtbhw7tyM1tDUDz5s0ZNPAMNmwoJje3Nd27fwmAwYPOZMOGTZmcpjSgF5ct56ILv0XHju0BaNu2Dccck5fWvk8vfI4rrywP5BdffB7LXnoVgNzc1iyYP4vxP76b11YU1s/EA5OsRQtRTeWRhcAX3b3o0A3R6+SlCp07d2L6tHvJzs4iKyuLefOe5plFz3Ptdbcw54kpJJPOnt17uCb/5kxPVWrp0dmTOevMb9ChQzv+trmQn9/1W5o2bQrAlIdn06lTR1atWEzr1l8kmUzywx98l69+rT9vv72Jn/7sNyxe9DhZWcaBA2X88IfjeffdrTWec/ojBcyccT8b1i9n9+49XP7t7wNw/fev4ssnHMf4O25k/B03AjDs3FHs3Pn5zakS9fze20zTi31FpNGoixf7Xn7shWnHnD+885Re7Csikklxr2kraItIrIRaq06XgraIxEqot6enS0FbRGJF5RERkYDEffWIgraIxIrKIyIiAdGFSBGRgMS9pq1Hs4pIrNTlSxDMbLqZvWdmb6X0/czMtppZUdTOTdl2u5kVm9lGMzsnpX9o1FdsZrel9B9vZqvMbJOZPWFmNT5XV0FbRGKl4rn16bQ0zACGVtI/yd17R20RgJn1BEYCvaJ9HjSzbDPLBiYDw4CewKhoLMA90bG6A7uBsTVNSEFbRGIlgafdauLuLwOlaZ56OFDg7vvc/a9AMdA3asXuvtnd9wMFwHArf97uQGBetP9MYERNJ1HQFpFYaaB3RI4zszei8knF2yjygC0pY0qivqr62wN73L3skP5qKWiLSKzUpjxiZvlmVpjS8tM4xUPACUBvYDswMeqv7OFTfgT91dLqERGJldpk0KkvbKnFPgdf7GpmD1P+CGsoz5S7pQztCmyLPlfWvwtoY2ZNomw7dXyVlGmLSKzU9zsizaxzytcLgYqVJQuAkWbWzMyOB7oDq4E1QPdopUgO5RcrF3j5ldBlwCXR/mOA+TWdX5m2iMRKXd7GbmaPA/2BDmZWAtwJ9Dez3pSXMv4GXAvg7uvMbA6wHigDrnf3RHScccASIBuY7u7rolPcChSY2QRgLTCtxjnpJQgi0ljUxUsQTssbmHbMeXXri3oJgohIJunZIyIiAanv6kGmKWiLSKwo0xYRCUjcHxiloC0isZLweD+cVUFbRGJFNW0RkYCopi0iEhDVtEVEApJUeUREJBzKtEVEAqLVIyIiAVF5REQkICqPiIgERJm2iEhAlGmLiAQkUf7egdhS0BaRWNFt7CIiAdFt7CIiAVGmLSISEK0eEREJiFaPiIgERLexi4gERDVtEZGAqKYtIhIQZdoiIgHROm0RkYAo0xYRCYhWj4iIBCTuFyKzMj0BEZG65O5pt5qY2VAz22hmxWZ2WwNMv0YK2iISK16Lf6pjZtnAZGAY0BMYZWY9G+AnVEtBW0RipQ4z7b5Asbtvdvf9QAEwvN5/QA1U0xaRWKnDmnYesCXlewlwal0d/EjVe9Au27/V6vscoTCzfHefkul5SOOiPxd1qzYxx8zygfyUrikp/1tUdpyMX+VUeaRh5dc8RD6H9OciQ9x9irv3SWmpf3mWAN1SvncFtjXsDA+noC0iUrk1QHczO97McoCRwIIMz0k1bRGRyrh7mZmNA5YA2cB0d1+X4WkpaDcw1S2lMvpz0Ui5+yJgUabnkcrifp++iEicqKYtIhIQBe0G0hhvh5XMMrPpZvaemb2V6blIOBS0G0BjvR1WMm4GMDTTk5CwKGg3jEZ5O6xklru/DJRmeh4SFgXthlHZ7bB5GZqLiARMQbthNMrbYUUkPAraDaNR3g4rIuFR0G4YjfJ2WBEJj4J2A3D3MqDidti3gTmN4XZYySwzexxYAZxoZiVmNjbTc5LGT3dEiogERJm2iEhAFLRFRAKioC0iEhAFbRGRgChoi4gEREFbRCQgCtoiIgFR0BYRCcj/AmtASWi9POBTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators=100)\n",
    "rf.fit(X_train_rf, y_train_rf.values.ravel())\n",
    "y_pred_rf = rf.predict(X_test_rf)\n",
    "y_test_rf = pd.DataFrame(y_test_rf)\n",
    "cm_rf = confusion_matrix(y_test_rf, y_pred_rf.round())\n",
    "sns.heatmap(cm_rf, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.998273\n",
       "1    0.001727\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc.Class.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in total dataset we have only 492 fraudlent records, which makes our dataset imbalanced or biased. 99% records are real transaction. Only 1% is fraud. So our data is not balanced. It may result in a model which is biased toward real transaction scenario. So we will apply undersampling and oversampling technique to overcome this conition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "fraud_indices = np.array(df_cc[df_cc.Class == 1].index) # All fraud indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "492"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fraud_indices) # total fraudlent records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284315"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#normal indices\n",
    "normal_indices = np.array(df_cc[df_cc.Class == 0].index)\n",
    "len(normal_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose random normal rows equal to fraud_indices. So that we can have equal data to train our model.\n",
    "random_normal_indices = np.random.choice(normal_indices, len(fraud_indices), replace=False)\n",
    "random_normal_indices = np.array(random_normal_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "492"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(random_normal_indices) # So we selected random normal indices equals to the number of total fraud rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "984"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "undersample_indices = np.concatenate([fraud_indices, random_normal_indices])\n",
    "len(undersample_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So now we have total 984 records with 492 - Fraud case and 492 real transaction case\n",
    "# lets create our dataset with these indices\n",
    "undersample_df = df_cc.iloc[undersample_indices, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d1c08a400>"
      ]
     },
     "execution_count": 45,
     "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": [
    "undersample_df.Class.value_counts().plot(kind=\"bar\") # So now our new undersampled dataset is balanced\n",
    "# Which is a good thing. So lets check whether it predict data in proper way or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_under = undersample_df.iloc[:, undersample_df.columns != 'Class']\n",
    "y_under = undersample_df.iloc[:, undersample_df.columns == 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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",
       "      <td>541</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>623</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4920</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6108</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6329</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Class\n",
       "541       1\n",
       "623       1\n",
       "4920      1\n",
       "6108      1\n",
       "6329      1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_under.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_us, X_test_us, y_train_us, y_test_us = train_test_split(X_under, y_under, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_us = np.array(X_train_us)\n",
    "X_test_us = np.array(X_test_us)\n",
    "y_train_us = np.array(y_train_us)\n",
    "y_test_us = np.array(y_test_us)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\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": [
    "clf.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "688/688 [==============================] - 0s 559us/step - loss: 0.5221 - accuracy: 0.8866\n",
      "Epoch 2/5\n",
      "688/688 [==============================] - 0s 163us/step - loss: 0.2048 - accuracy: 0.9259\n",
      "Epoch 3/5\n",
      "688/688 [==============================] - 0s 165us/step - loss: 0.1771 - accuracy: 0.9375\n",
      "Epoch 4/5\n",
      "688/688 [==============================] - 0s 161us/step - loss: 0.1542 - accuracy: 0.9404\n",
      "Epoch 5/5\n",
      "688/688 [==============================] - 0s 151us/step - loss: 0.1259 - accuracy: 0.9448\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f6d100f7b00>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
    "clf.fit(X_train_us, y_train_us, batch_size=15, nb_epoch=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_us = clf.predict(X_test_us)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cd56136a0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "cm_us = confusion_matrix(y_test_us, y_pred_us.round() )\n",
    "sns.heatmap(cm_us, annot=True)\n",
    "# Wow we literally reduced the number of false negative. But lets check for whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 1)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cd545c4a8>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "y_pred = clf.predict(X)\n",
    "y_expected = pd.DataFrame(y)\n",
    "\n",
    "cm_us = confusion_matrix(y_expected, y_pred.round() )\n",
    "sns.heatmap(cm_us, annot=True)\n",
    "# So we have better result than previous. Lets try to improve it more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oversampling /SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SMOTE is the technique for oversampling, where we equalize data by repeating it. We will use SMOTE method for it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Install SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment the below line to install library to use SMOTE.\n",
    "# !pip install -U imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 29)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating OVERSAMPLE data\n",
    "X_oversample, y_oversample = SMOTE().fit_sample(X, y.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting array into dataframe\n",
    "X_oversample = pd.DataFrame(X_oversample)\n",
    "y_oversample = pd.DataFrame(y_oversample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>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>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251412</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>0.244964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</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>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069083</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>-0.342475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</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>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524980</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>1.160686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</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>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208038</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>0.140534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</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>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408542</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>-0.073403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\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",
       "         7         8         9   ...        19        20        21        22  \\\n",
       "0  0.098698  0.363787  0.090794  ...  0.251412 -0.018307  0.277838 -0.110474   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.069083 -0.225775 -0.638672  0.101288   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.524980  0.247998  0.771679  0.909412   \n",
       "3  0.377436 -1.387024 -0.054952  ... -0.208038 -0.108300  0.005274 -0.190321   \n",
       "4 -0.270533  0.817739  0.753074  ...  0.408542 -0.009431  0.798278 -0.137458   \n",
       "\n",
       "         23        24        25        26        27        28  \n",
       "0  0.066928  0.128539 -0.189115  0.133558 -0.021053  0.244964  \n",
       "1 -0.339846  0.167170  0.125895 -0.008983  0.014724 -0.342475  \n",
       "2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  1.160686  \n",
       "3 -1.175575  0.647376 -0.221929  0.062723  0.061458  0.140534  \n",
       "4  0.141267 -0.206010  0.502292  0.219422  0.215153 -0.073403  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_oversample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(568630, 29)\n",
      "(568630, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_oversample.shape)\n",
    "print(y_oversample.shape)\n",
    "# Now we can see we have much more data to train because of oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get train test data\n",
    "X_train_smote, X_test_smote, y_train_smote, y_test_smote = train_test_split(X_oversample, y_oversample, \n",
    "                                                                            test_size=0.3)\n",
    "X_test_smote = np.array(X_test_smote)\n",
    "X_train_smote = np.array(X_train_smote)\n",
    "y_train_smote = np.array(y_train_smote)\n",
    "y_test_smote = np.array(y_test_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "398041/398041 [==============================] - 67s 168us/step - loss: 0.0279 - accuracy: 0.9908\n",
      "Epoch 2/5\n",
      "398041/398041 [==============================] - 63s 159us/step - loss: 0.0135 - accuracy: 0.9964\n",
      "Epoch 3/5\n",
      "398041/398041 [==============================] - 63s 159us/step - loss: 0.0110 - accuracy: 0.9973\n",
      "Epoch 4/5\n",
      "398041/398041 [==============================] - 63s 158us/step - loss: 0.0097 - accuracy: 0.9977\n",
      "Epoch 5/5\n",
      "398041/398041 [==============================] - 62s 156us/step - loss: 0.0090 - accuracy: 0.9979\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f6cd4044978>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit model for SMOTE data\n",
    "clf.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
    "clf.fit(X_train_smote, y_train_smote, batch_size=15, nb_epoch=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_smote = clf.predict(X_test_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cd3992a58>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W3FNfimNm9YBzgGEpHDoLGOUJM4BqZlYb6AhMdfd1UaCeCmRG6w5x94/c3YFRQLfiDqKgLSKx4gWW8mJm2WY2J2nJ3mV3DwE3sPtLzO6OSiCDzaxy1FYXWJbUJzdqK6o9t5D2Iqk8IiKxkl+Qei7q7kOAIYWtM7NzgdXu/rGZtU1adTPwDVAp2vZG4C6gsLqM70V7kZRpi0islGJ5pDXQ1cy+BMYCZ5rZaHdfGZVAtgBPA82j/rlA/aTt6wErimmvV0h7kRS0RSRWCtxSXori7je7ez13/znQE3jH3S+KatFEMz26AZ9Gm0wCekezSFoCG919JTAFONvMqptZdeBsYEq0bpOZtYz21RuYWNz5qTwiIrFSDjfXPGdmtUiUN+YDf4jaJwOdgRzgB6BvYjy+zsz+AsyO+t3l7uuiz5cDI4EqJGaNFDlzBMC8jG/U37Z6ScyfBCB7o0q9tukeguyH8rYu3+eIO7d+Vsoxp+myicHdiVPmmbb+55TCbF7xfrqHIDFVXNkjdCqPiEislGT2SIgUtEUkVuJej1XQFpFYUXlERCQgejSriEhAYv4ydgVtEYkXL/Tu8PhQ0BaRWMlTeUREJBzKtEVEAqKatohIQJRpi4gERJm2iEhA8pVpi4iEo/Tf67t/UdAWkVgpUKYtIhIOPTBKRCQguhApIhKQAlN5REQkGPnpHkAZU9AWkVjR7BERkYBo9oiISEA0e0REJCAqj4iIBERT/kREApKvTFtEJBzKtEVEAqKgLSISkJi/IlJBW0TiJe6ZdoV0D0BEpDTll2ApipkdYGazzOxfZrbQzO6M2huY2UwzW2Jm48ysUtReOfqeE63/edK+bo7aF5tZx6T2zKgtx8xuSuX8FLRFJFYKLPWlGFuAM939JKAJkGlmLYH7gMHu3hBYD/SL+vcD1rv7McDgqB9mdjzQE2gMZAJPmFmGmWUAjwOdgOOBXlHfIiloi0isFJRgKYonfBd9/Vm0OHAmMCFqfwboFn3Oir4TrW9vZha1j3X3Le7+BZADNI+WHHdf6u5bgbFR3yIpaItIrJQkaJtZtpnNSVqyk/cVZcTzgdXAVODfwAZ3z4u65AJ1o891gWUA0fqNwGHJ7btss6f2IulCpIjESkmePeLuQ4AhRazPB5qYWTXgJeC4Ig5ZWMHFi2gvLGkudvgK2iISK2Xx7BF332Bm7wItgWpmVjHKpusBK6JuuUB9INfMKgKHAuuS2rdL3mZP7Xuk8oiIxEopzh6pFWXYmFkV4CzgM2Aa0CPq1geYGH2eFH0nWv+Ou3vU3jOaXdIAaAjMAmYDDaPZKJVIXKycVNz5KdMWkVgpKL2Hs9YGnolmeVQAxrv7q2a2CBhrZoOAecDwqP9w4FkzyyGRYfcEcPeFZjYeWATkAVdEZRfMbAAwBcgARrj7wuIGpaAtIrFSWjfXuPsC4ORC2peSmPmxa/uPwPl72NfdwN2FtE8GJpdkXAraIhIregmCiEhA4n4bu4K2iMRKnsU711bQFpFYiXfIVtAWkZhReUREJCClOOVvv6SgLSKxEu+QraAtIjGj8oiISEDyY55rK2iLSKwo0xYRCYgr0xYRCUfcM209mrWcdDy7LQs/fY/PF03nhuuvSPdwZB+MGvsSWb+7jG4X/YHrb7+XLVu27rT+5demcvo5F3Jenys4r88VTJj0xj4fc+O3m+h/1S10vrAf/a+6hY3fbtpp/SefLebE08/hzWnv7/OxQleAp7yESEG7HFSoUIFHHr6bc7tcxAkntePCC7tx3HEN0z0s2Qur1qzluQkTGTfiEV4e/RQFBQW8/tY/d+uXeWYbXnzmcV585nF6dM1Mef+z5i5g4KAHdmsf9ux4Wp7ShMnjhtPylCYMHz1+x7r8/HwGP/E0rZs33buTihkvwRIiBe1y0PzXJ/Pvf3/JF198zbZt2xg/fiJdu3RM97BkL+Xl57Nly1by8vLZ/OMWatWskfK2I56bwIX9/pfuvS/nsWHPprzdtPc/IqvTWQBkdTqLd977aMe65ydMokPb1tSoXi31k4ixPDzlJUR7HbTNrG9pDiTO6tQ9kmW5/32LUO7yldSpc2QaRyR764haNbmk13mc9ZvetMv6LQcfWJXWLZrt1m/qP6fTvfflXDNwECtXrQHgg5kf83XucsYOe5gXRz7OosU5zJn/SUrH/c/6DTt+OdSqWYN1GzYCicz/7fc+5IJunUvpDMPnJfgTon25EHkn8HRhK6I3GmcDWMahVKhw4D4cJnxmu7+0LvEWIgnNxm83Me39GUx54WkOPvggrrv1Hl6Z8g5dOp65o0/b01rQuUMbKlWqxLiXXmPgoAcY8ei9fDh7Lh/OmkuPSwYA8MPmzXy1bAWnNDmBXr+/mq1bt/HD5s1s/HYT5/VJXPe49o+XFvpLYbv7Hv4711x+KRkZGWV74gGJ+4XIIoO2mS3Y0yrgiD1tl/yG44qV6v7ko9Py3JXUr1dnx/d6dWuzcuWqNI5I9taMOfOpW+eIHaWI9m1aMf+TRTsF7WqHHrLjc4+umQx+ckTii0P/iy8sNCseM/QhIFHTnjh5Knffet1O6w+rXo01a9dRq2YN1qxdR41qhwKw8PMlXH/7vQCs3/gt7380m4yMDNqf0ar0TjowoWbQqSou0z4C6Ais36XdgA/LZEQxNHvOfI45pgE//3l9li//hgsuyOLi3ppBEqLaR9Riwaefs/nHHzmgcmVmzplP40Y7X1TeHlwBpk2fwdFHJV643ap5Ux4b9iznnt2OqlWrsGrNWipWrMhhKdSi257Wkomvv0X/iy9g4utv0e70UwGYMmHkjj4DBz1Am9bNf9IBG37imTbwKnCQu8/fdUX0OnlJQX5+PlddfSuTX3uejAoVGPnMOBYt+r90D0v2womNG9Gh3Wlc0PdKMjIyaPTLX3B+ViceGzqKxo1+SbvTWzL6hYm8O30GGRUzOPTggxkUZc2tWzRj6VfL+N1l1wJQtcoB/PXP16cUtPtffAHX3XYP/3h1CrWPqMWDgwaW6XmGLD/mpUcr69qqyiNSmM0rNJ9YdvezmkfvfgGohH57VPeUY87zX720z8crb7ojUkRi5ade0xYRCcpPvaYtIhKUUG9PT5WCtojEisojIiIBifvsEQVtEYkVlUdERAKiC5EiIgGJe01bj2YVkVgpzZcgmNkIM1ttZp8mtd1hZsvNbH60dE5ad7OZ5ZjZYjPrmNSeGbXlmNlNSe0NzGymmS0xs3FmVqm4MSloi0isuHvKSwpGAoW9xWKwuzeJlskAZnY80BNoHG3zhJllmFkG8DjQCTge6BX1Bbgv2ldDEs946lfcgBS0RSRW8vGUl+K4+3vAuhQPnQWMdfct7v4FkAM0j5Ycd1/q7luBsUCWJZ7ZfCYwIdr+GaBbcQdR0BaRWCmnd0QOMLMFUfmketRWF1iW1Cc3attT+2HABnfP26W9SAraIhIrJSmPmFm2mc1JWrJTOMSTwC+AJsBKYPtLPQt7+JTvRXuRNHtERGKlJBl08gtbSrDNjjeYmNlQEo+whkSmXD+paz1g+3sGC2tfC1Qzs4pRtp3cf4+UaYtIrJT1OyLNrHbS1+7A9pklk4CeZlbZzBoADYFZwGygYTRTpBKJi5WTPHEldBrQI9q+DzCxuOMr0xaRWCnN29jNbAzQFqhpZrnA7UBbM2tCopTxJXAZgLsvNLPxwCIgD7jC3fOj/QwApgAZwAh3Xxgd4kZgrJkNAuYBw4sdk16CIOmglyBIYUrjJQit656Zcsz5YPk7egmCiEg66dkjIiIBKevqQbopaItIrCjTFhEJSNwfGKWgLSKxku/xfjirgraIxIpq2iIiAVFNW0QkIKppi4gEpEDlERGRcCjTFhEJiGaPiIgEROUREZGAqDwiIhIQZdoiIgFRpi0iEpD8xHsHYktBW0RiRbexi4gERLexi4gERJm2iEhANHtERCQgmj0iIhIQ3cYuIhIQ1bRFRAKimraISECUaYuIBETztEVEAqJMW0QkIJo9IiISkLhfiKyQ7gGIiJQmd095KY6ZZZrZYjPLMbObymH4xVLQFpFY8RL8KYqZZQCPA52A44FeZnZ8OZxCkRS0RSRWSjHTbg7kuPtSd98KjAWyyvwEiqGatojESinWtOsCy5K+5wItSmvne6vMg3be1uVW1scIhZllu/uQdI9D9i/6uShdJYk5ZpYNZCc1DUn6uyhsP2m/yqnySPnKLr6L/ATp5yJN3H2Iu5+StCT/8swF6id9rwesKN8R7k5BW0SkcLOBhmbWwMwqAT2BSWkek2raIiKFcfc8MxsATAEygBHuvjDNw1LQLmeqW0ph9HOxn3L3ycDkdI8jmcX9Pn0RkThRTVtEJCAK2uVkf7wdVtLLzEaY2Woz+zTdY5FwKGiXg/31dlhJu5FAZroHIWFR0C4f++XtsJJe7v4esC7d45CwKGiXj8Juh62bprGISMAUtMvHfnk7rIiER0G7fOyXt8OKSHgUtMvHfnk7rIiER0G7HLh7HrD9dtjPgPH7w+2wkl5mNgb4CDjWzHLNrF+6xyT7P90RKSISEGXaIiIBUdAWEQmIgraISEAUtEVEAqKgLSISEAVtEZGAKGiLiAREQVtEJCD/D6hKJfV0D39EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_smote = confusion_matrix(pd.DataFrame(y_test_smote), y_predict_smote.round())\n",
    "sns.heatmap(cm_smote, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So finally we reduced it. We will stop here We can do other changes like we can change the epoch,\n",
    "# or change the batch size. Change test_size. Try them all and tell us the best model you got..\n",
    "# Thank You... :) SHARE AND UPVOT\n"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}