Fraud_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Credit Card Fraud Detection \n",
    "You can download the data from https://www.kaggle.com/mlg-ulb/creditcardfraud. \n",
    "##### Context\n",
    "It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.\n",
    "##### Content\n",
    "The datasets contains transactions made by credit cards in September 2013 by european cardholders. \n",
    "This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.\n",
    "\n",
    "It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, f1_score, recall_score, confusion_matrix, classification_report\n",
    "\n",
    "import tensorflow\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.models import Sequential\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"creditcard.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the privacy reason, we only know Time and Amount. The other 28 columns from V1 to V28 are already scaled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().max(), df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284807.000000\n",
       "mean      94813.859575\n",
       "std       47488.145955\n",
       "min           0.000000\n",
       "25%       54201.500000\n",
       "50%       84692.000000\n",
       "75%      139320.500000\n",
       "max      172792.000000\n",
       "Name: Time, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Time'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descriptive statstics for 'Time' independent feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:, 'Time'] = df['Time'] / 3600\n",
    "duration_by_day = df['Time'].max() / 24\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that duration of all the transactions is 2 days. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Hourly')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9))\n",
    "plt.hist(df['Time'], bins=48)\n",
    "plt.xlim([-2,50])\n",
    "plt.xticks(np.arange(0, 48, 4))\n",
    "plt.title('Number of Transactions with Times in Two Days')\n",
    "plt.ylabel('Number of Transactions')\n",
    "plt.xlabel('Hourly')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descriptive statstics for 'Amount' independent feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284807.000000\n",
       "mean         88.349619\n",
       "std         250.120109\n",
       "min           0.000000\n",
       "25%           5.600000\n",
       "50%          22.000000\n",
       "75%          77.165000\n",
       "max       25691.160000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Amount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Transaction Amounts')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7cAAAIlCAYAAAAOvQ//AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAMTQAADE0B0s6tTgAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde7RlZ1kn6t8LpQGpFiXkolSuEFDEGGnSBE8EvEALg4YmwSDdQERHA6djh6ZG2thgN47WHodDmuBhEJUcobkeCJfuNIRLK6OJgBBBRyIJCgmGoqgAIQSRLgKEMu/5Y81tVjZVxa59rW/v5xljjr3m987L9+25amb/Mueaq7o7AAAAMLK7bXQHAAAAYKWEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAWADVNWbq+oPN7ofALBZCLcAHDaqqr/L9OiN7uNyVNWXq+qXFjU/J8nOddr/m6vq76vqn6zH/lZDVb2tqv5go/sBwDi2bXQHAGDOD829vjDJw5OcNdf2lcUrVNUR3f2tte7Yauvuv1uP/VTVvZM8LsklSc5N8tH12C8ArDdXbgE4bHT3FxemJF9Pcvt8W3ffXlUvrqr3VdWFVfWFJB9Kkqr6j1X1qaq6raqur6rnzm+7qq6qqt+pqldV1d6qurGqzpqrH1NV/62q/naqX11VD5tqD6mq/1lVt1bVV6rqHVV13KLtP7yq/qSqvjEt95aF/SY5MsmbpqvP753a73JbclX9cFVdXlVfn/bxyqq651L7fxDnTL+jVyT5par63kX9/mJV/dtpTLdV1bVVdVpVPbSq/nza1/+YQvLCOt9fVa+tqr+rqv9dVZdV1X0X9fU397Ofp0+vf2T6XTyhqv5iGvP7qup+U/3FSc5O8pxpuW9O7Q+sqj+uqq9N+/5IVZ2whN8BAFuAcAvAiM5I8pAkP5fkGVPbbUl+JcmPJfmtJC+rqp9dtN55Sa5OclqSy5K8tqp+cKr9X0m+J8mZU/0/J/n2VNue5I1JfirJo5PcfZpPMgumSd6X5BNJTk/yM7nzCunjM7vi/NzMrkw/7QBjelOS+077f3KSxyb5vw+h/wdybpI3d/f1ST6b5J/tZ5kLk/x/03Z3J3lDkv+S5N8meWSSU5P8+tzyr0jyT6ax/UySByR59Xfpx/78VpLnZ3Y8j0zykqn9d5JckeR1mf3OFgLsHyTZleQfT/v//SS9jP0CsAm5LRmAEd2e5Nnd/Y2Fhu7+L3P1z1TVY5M8Jcn/mmt/f3e/Ikmq6kVJLsgsKL0vyXFJ/qi7PzEt++m5bV+V5KqF+ao6b9rH0d39pSTPS/Kp7v7Xc/v6+LTuV6qqk/zddEX6O1TVaZmFyAd0999Mbc9PcllVXTg3zoP1f3/bvX+Shya5fGp6c2Zh9+2LFn1Td795WueiJO9P8hvdvXBV/LVJHjO9PjLJv0zymO7+06ntV5NcXVX3X+j/Ev2n7v7AtI2XJ/ntJOnuvVX1rSTfWPQ7Oy7J73f3DdP8pw5hXwBscq7cAjCiT84H2ySpqn9eVR+uqi9V1d4k/yKzMDTv2oUX3X17ZldUj56aLk3y29OtxS+sqgfMbfs+023CN1TV15JcN5UWtv+QJFeuYDwPSnLLomD4kSTfm+SkJfZ/f85N8p7u/t/T/JuT/NOqOmrRctfOvb55+vmJRW0L6zwgs78f/iHsd/c1Sb4xjeNQzO/3izn4WJLk95K8oareU1U7F25jBoBEuAVgTLfNz1TVjyR5a5L3ZPbwpIXbdr9n0XrfXjTfmf5b2N1vTXL/zALgGUmuq6p/Pi338swebnX+VPuZqX1h+7Wy4Sx5/QP2/zs2WFVJnp7kyVW1r6r2Jbkxs8D8Lw6y3T5A28J+ltLXO/az3OJjcbB97Fd3vyyz/5Hwx5ndXv2pqnr4EvoDwBYg3AKwGTwsya3d/dvd/Rfd/ekkJx/qRrr7pu7+/e7+Z5l9BvWZU+mMJH/Q3e/p7r9K8gOLVr02yaMOsulvZ/Y53QP5ZJKjqmq+z4/I7PbrzxzKGOY8MrMroT+ZWdhfmF6U2RXd5bohs/B6xkLDdFv1PXPnbcK3JDl2rn6/JPc5xP3s93fW3Td098XdvfC55qce4nYB2KSEWwA2g7/JLBz+y6p6QFX9dpIfP5QNTE8ifnxVnVSz74M9I3eGtb9J8tTpKb+PyuzhU/P+nyQ/UlW/V1U/Nk3z32H72SSPqtkTmb9/8b6n23o/kNkDon5y2sfFSV65+PbrQ3Bukvd2919293ULU2YPfvrJqjqk389cX2/N7IFTr6iqn6rZE6VfleSdc7dVfyDJOVX1qKo6NbNbvg/165o+m+QfV9UJ0+d8U1Uvr6qfraoTa/adxw+Oz90CMBFuARhed38ks4cR/W6Sv8jsqcP/9RA3c8e0/l8neUeSP0nyn6ba8zK7Mnl1Zt8X+x8W7f/zmT3d+NRp/3+S2VOTF/yHzJ6yfFOStxxg/09LcmtmX9tzeWa33l54iGNIklTV92X2MK3LF9e6e8/Ux5Vcvf03ST6W2W3g78/s4Vu/Mlf/van2jml6VZKvHuI+fj+zz/F+MrPfW5LcI7Nw/qkkr5+2+/8uawQAbDrV7Qn6AAAAjM2VWwAAAIYn3AIAADC8JYfbqvqjqvp4VV1TVR+sqtOq6h5VdXlVXT+1v7eqTpxb58qqunGqXTN9If1C7ehp+Ruq6rqqOnOlNQAAALambYew7Dnd/dUkmb7379VJfiqzJyC+p7u7qn5tmn/s3Hrnd/cV+9nei5Nc1d2/UFWnJ3lbVd2/u/etoAYAAMAWtOQrtwvBdnLvJHd09ze7+91951OprsrSv1fwnMyeOJnu/liSm5OcucIaAAAAW9Ahfea2ql5XVZ9L8jvZ/1cInJ/knYvaLqqqa6vqsoUvp5++r+5u3X3L3HK7khy/3NqhjAMAAIDN5VBuS053PzNJqurcJBclefxCrapekOSUJM+dW+UZ3f25qqok5yW5IrMvXE+Sxd9BVPO7WmbtzsaqnUl2Lszf/e53v9+xxx67v0UBAABYQzfddNPt3X3EWu5j2d9zW1XfSLKju2+tqguS/FKSn190+/Lidb6Z5H7TOl9PcuLCVdiq+miSX+/uK5dbO1h/d+zY0Xv27FnWWAEAAFi+qrqpu3es5T6WdFtyVX1/Vf3w3PyTk9ya5CvTFdKnJXnMfLCtqm1Vdczc/NlJbu7uW6emt2Z2NTfTg6GOTfKhFdYAAADYgpZ6W/K9k7y9qu6Z5I4ktyR5QpL7JXlpkhuTvH9293G+1d0PT3JEkndV1RHTOl9O8sS5bV6Y5PVVdUOS2zO7hXnfCmsAAABsQcu+LXk0bksGAADYGIfNbckAAABwOBNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIa35HBbVX9UVR+vqmuq6oNVddrUfkpVfbiqrq+qj1bVg+fWWdcaAAAAW9OhXLk9p7tP7e7Tkrw0yaun9lcmubS7H5jkJUleNbfOetcAAADYgqq7D32lqnOT/Jskj09yfZL7dve+qqokX0hyRpLb1rPW3bsO1ucdO3b0nj17DnmsAAAArExV3dTdO9ZyH9sOZeGqel2Sn5lmfyHJcUk+3937kqS7u6p2Jzk+ydfXubZrmb8DAAAABndID5Tq7md293FJfjPJRQvNixar+VXWuXZnY9XOqtqzMO3du3d/iwEAALAJLOtpyd392syu4O5JsqOqtiXJdJvwcUl2J/ncOtcW9/Hi7t6xMG3fvn05QwUAAGAASwq3VfX9VfXDc/NPTnJrki8luTrJ06fS2Ul2dfeu7l7X2iGOGwAAgE1kSQ+Uqqrjkrw9yT2T3JHkliQXdPc1VfWgJK9JcmSSryU5t7s/Ma23rrWD8UApAACAjbEeD5Ra1tOSRyTcAgAAbIz1CLfL+swtAAAAHE6EWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPC2bXQHtrxLL93oHgDr7dnP3ugeAABsOq7cAgAAMDzhFgAAgOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAAhifcAgAAMDzhFgAAgOEtKdxW1T2q6vKqur6qrqmq91bViVPtw1PbNVV1XVV1VZ061V5TVXvm6hfNbfP7qupNVfXpabtnrbQGAADA1rTtEJa9NMl7urur6tem+cd2908tLFBVT0nyou7++Nx6L+7uV+xnexck+VZ3P6CqTkrykap6f3f/7QpqAAAAbEFLunLb3d/s7nd3d09NVyU5eT+L/kqSVy1x309Ncsm0/c8k+UCSJ62wBgAAwBa03M/cnp/knfMNVXW/JI9O8oZFy+6sqo9X1RVVddpc+/FJPjs3v2tqW0kNAACALehQbktOklTVC5KckuS5i0q/nOSK7v7yXNsLk3yhu++oqicneU9VndLde6d6zy1bi7a33NpCP3cm2bkwf+9733t/iwEAALAJHNKV26q6IMlZSR7X3bfNtVeSZ2XRLcndfVN33zG9/u9JvpbkQVN5d5IT5xY/YWpbSW1+3xd3946Fafv27UseJwAAAGNZcridroQ+Lcljuvuri8qPSvK9Sf540To75l6fkeTIJJ+emt6a5LypdtK0jXessAYAAMAWtKTbkqeQ+tIkNyZ5/+xCbb7V3Q+fFvnVJP914SrtnNdU1TFJ/j7JN5L8Ynf/3VS7KMmrq+rTSe5Icl53f2WFNQAAALagJYXb7t6TA3y2dao/4wDtP3+Qdb6e2ZOPV60GAADA1rTcpyUDAADAYUO4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABjeksJtVd2jqi6vquur6pqqem9VnTjVrqyqG6f2a6rq+XPrHT0te0NVXVdVZ65lDQAAgK3pUK7cXprkQd19WpIrpvkF53f3adP0srn2Fye5qrtPSfKsJG+sqm1rWAMAAGALWlK47e5vdve7u7unpquSnLyEVc9Jcsm0jY8luTnJmWtYAwAAYAta7mduz0/yzrn5i6rq2qq6rKpOTpKqOjLJ3br7lrnldiU5fi1qyxwHAAAAm8Ahh9uqekGSU5K8cGp6Rnf/aJJTk3wws1uWF/Ti1de4Nt/PnVW1Z2Hau3fv/hYDAABgEzikcFtVFyQ5K8njuvu2JOnuz00/u7tfkeTkqjqyu2+d1jlqbhMnJNm9FrXFfe3ui7t7x8K0ffv2QxkqAAAAA1lyuK2qnUmeluQx3f3VqW1bVR0zt8zZSW5eCKFJ3prkvKl2epJjk3xoDWsAAABsQUt6ynBV7Ujy0iQ3Jnl/VSXJt5L8bJJ3VdURSe5I8uUkT5xb9cIkr6+qG5LcntktzPvWsAYAAMAWtKRw2917coDPtiZ52EHWuznJY9erBgAAwNa03KclAwAAwGFDuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAAhifcAgAAMDzhFgAAgOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAAhifcAgAAMDzhFgAAgOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAAhifcAgAAMDzhFgAAgOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAY3pLCbVXdo6our6rrq+qaqnpvVZ041V5dVZ+a2j9QVafNrfeaqtoz1a6pqovmat9XVW+qqk9P2z1rpTUAAAC2pm2HsOylSd7T3V1VvzbNPzbJ5Ume3d37quoJSd6S5IFz6724u1+xn+1dkORb3f2AqjopyUeq6v3d/bcrqAEAALAFLenKbXd/s7vf3d09NV2V5OSp9o7u3jfXfkJVLWW7T01yybSNzyT5QJInrbAGAADAFrTcz9yen+Sd+2l/XpJ3d/cdc207q+rjVXXF/C3LSY5P8tm5+V1T20pqAAAAbEGHcltykqSqXpDklCTPXdT+9CTnJPnpueYXJvlCd99RVU9O8p6qOqW79071nt/Eol0tt7bQn51Jdi7M3/ve997/gAAAABjeIV25raoLkpyV5HHdfdtc+1OTvCjJY7r7Swvt3X3TwlXc7v7vSb6W5EFTeXeSE+c2f8LUtpLaP+jui7t7x8K0ffv2QxkqAAAAA1lyuJ2uhD4tswD71bn2c5L8TpKf7+7di9bZMff6jCRHJvn01PTWJOdNtZOSPCrJO1ZYAwAAYAta0m3JU0h9aZIbk7y/qpLZE4sfnuSNSb6Y5H9M7Unyc919a5LXVNUxSf4+yTeS/GJ3/920zEVJXl1Vn05yR5LzuvsrK6wBAACwBS0p3Hb3nhzgs63d/T0HWe/nD1L7emZPPl61GgAAAFvTcp+WDAAAAIcN4RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAw1tSuK2qe1TV5VV1fVVdU1XvraoTp9rR0/wNVXVdVZ05t9661gAAANiaDuXK7aVJHtTdpyW5YppPkhcnuaq7T0nyrCRvrKptG1QDAABgC1pSuO3ub3b3u7u7p6arkpw8vT4nySXTch9LcnOSMzeoBgAAwBa03M/cnp/knVV1ZJK7dfctc7VdSY5f79riDlbVzqraszDt3bt3mUMFAADgcHfI4baqXpDklCQvnJp68SJzr9e7dudC3Rd3946Fafv27ftbDAAAgE3gkMJtVV2Q5Kwkj+vu27r71qn9qLnFTkiye71rhzIOAAAANpclh9uq2pnkaUke091fnSu9Ncl50zKnJzk2yYc2qAYAAMAWtKSnDFfVjiQvTXJjkvdXVZJ8q7sfnuTCJK+vqhuS3J7kGd29b1p1vWsAAABsQUsKt929Jwf+bOvNSR57ONQAAADYmpb7tGQAAAA4bAi3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMNbUritqpdX1a6q6qp6yNT2A1V1zdx0fVXtq6r7TPUrq+rGufrz57Z3dFW9t6puqKrrqurMldYAAADYurYtcbm3JXlJkg8tNHT3V5OctjBfVRckeVR3f2VuvfO7+4r9bO/FSa7q7l+oqtOTvK2q7t/d+1ZQAwAAYItaUrjt7g8kSVUdbLFnJXnhEvd7TpKTpm1/rKpuTnJmkitXUAMAAGCLWpXP3FbVI5IcmWTxVdqLquraqrqsqk6elj0yyd26+5a55XYlOX65tdUYAwAAAONarQdK/UqS1y26PfgZ3f2jSU5N8sHcNfj2ovVrFWp3LVTtrKo9C9PevXsPOgAAAADGteJwW1X3SvLUJK+eb+/uz00/u7tfkeTkqjqyu2+d1jtqbvETkuxebm1//erui7t7x8K0ffv2FY0TAACAw9dqXLn9xSQf7+5PLjRU1baqOmZu/uwkNy8E1CRvTXLeVDs9ybG582FVy60BAACwRS3pgVJVdUmSJ2UWJt9XVXu7+wFT+VeTvGrRKkckeVdVHZHkjiRfTvLEufqFSV5fVTckuT2zW5j3rbAGAADAFrXUpyWfl+mK6X5qP72ftq8nedhBtndzkseuZg0AAICta7UeKAUAAAAbRrgFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGN6Swm1VvbyqdlVVV9VD5tp3VdUnq+qaaXrqXO2UqvpwVV1fVR+tqgevZQ0AAICta6lXbt+W5Mwkn91P7Sndfdo0XTbX/sokl3b3A5O8JMmr1rgGAADAFrWkcNvdH+juPUvdaFUdneShSd4wNb09yUlVdeJa1JbaLwAAADan1fjM7Rur6tqq+sOqOmpqOy7J57t7X5J0dyfZneT4NaoBAACwha003D6yu38isyuqtyZ57VytFy1ba1y7a6FqZ1XtWZj27t17oEUBAAAY3IrCbXfvnn5+O8nvJvnpqfS5JDuqaluSVFVlduV19xrV9te3i7t7x8K0ffv2lQwVAACAw9iyw21V3auqfmCu6WlJrk6S7v7S9PrpU+3sJLu6e9da1JY7BgAAADaHbUtZqKouSfKkJMcmeV9V7U3y2CRvr6q7Z3Z78I1Jnjm32nOSvKaqXpDka0nOXeMaAAAAW1TNnsu0+e3YsaP37FnyA5/Xz6WXbnQPgPX27GdvdA8AANZVVd3U3TvWch+r8bRkAAAA2FDCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwlhRuq+rlVbWrqrqqHjK13aOqLq+q66vqmqp6b1WdOLfOlVV141S7pqqeP1c7elr+hqq6rqrOXGkNAACArWupV27fluTMJJ9d1H5pkgd192lJrpjm553f3adN08vm2l+c5KruPiXJs5K8saq2rbAGAADAFrWkcNvdH+juPYvavtnd7+7unpquSnLyEvd7TpJLpu18LMnNmYXnldQAAADYolbzM7fnJ3nnoraLquraqrqsqk5Okqo6MsnduvuWueV2JTl+ubVVHAMAAAADWpVwW1UvSHJKkhfONT+ju380yalJPpjZbcsLOndVq1Bb3KedVbVnYdq7d+/BhgAAAMDAVhxuq+qCJGcleVx337bQ3t2fm352d78iyclVdWR33zqtd9TcZk5Isnu5tf31q7sv7u4dC9P27dtXOlQAAAAOUysKt1W1M8nTkjymu786176tqo6Zmz87yc0LATXJW5OcN9VOT3Jskg+tsAYAAMAWtaQnDVfVJUmelFmYfF9V7U3y6CQvTXJjkvdXVZJ8q7sfnuSIJO+qqiOS3JHky0meOLfJC5O8vqpuSHJ7Zrcw71thDQAAgC1qSeG2u8/LdMV0kf1+5rW7v57kYQfZ3s1JHruaNQAAALau1XxaMgAAAGwI4RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeEsKt1X18qraVVVdVQ+Zaz+lqj5cVddX1Uer6sEbVQMAAGDrWuqV27clOTPJZxe1vzLJpd39wCQvSfKqDawBAACwRVV3L33hql1JntDd11XV0UmuT3Lf7t5XVZXkC0nOSHLbeta6e9d36/uOHTt6z549Sx7rurn00o3uAbDenv3sje4BAMC6qqqbunvHWu5j2wrWPS7J57t7X5J0d1fV7iTHJ/n6Otd2rWAcAAAADG6lD5RafNm3NrB210LVzqraszDt3bv3QIsCAAAwuJWE288l2VFV25Jkuk34uCS7N6D2Hbr74u7esTBt3759BUMFAADgcLbscNvdX0pydZKnT01nJ9nV3bvWu7bcMQAAALA5LOmBUlV1SZInJTk2yZeT7O3uB1TVg5K8JsmRSb6W5Nzu/sS0zrrWvhsPlAIOGx4oBQBsMevxQKlDelryyIRb4LAh3AIAW8x6hNuVPlAKAAAANpxwCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwxNuAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADC8FYfbqvqBqrpmbrq+qvZV1X2q6sqqunGu9vy59Y6uqvdW1Q1VdV1VnbnSGgAAAFvTtpVuoLu/muS0hfmquiDJo7r7K1WVJOd39xX7WfXFSa7q7l+oqtOTvK2q7t/d+1ZQAwAAYAtai9uSn5XkVUtY7pwklyRJd38syayw9SUAABSWSURBVM1JzlxhDQAAgC1oVcNtVT0iyZFJ5q/UXlRV11bVZVV18rTckUnu1t23zC23K8nxy62t5jgAAAAYy2pfuf2VJK+bu0X4Gd39o0lOTfLB3DX09qJ1axVqdzZW7ayqPQvT3r17lzQAAAAAxrNq4baq7pXkqUlevdDW3Z+bfnZ3vyLJyVV1ZHffOq1z1NwmTkiye7m1xf3p7ou7e8fCtH379lUZJwAAAIef1bxy+4tJPt7dn0ySqtpWVccsFKvq7CQ3LwTUJG9Nct5UOz3JsUk+tMIaAAAAW9CKn5Y851dz1wdJHZHkXVV1RJI7knw5yRPn6hcmeX1V3ZDk9sxuYd63whoAAABb0KqF2+7+6UXzX0/ysIMsf3OSx65mDQAAgK1pLb4KCAAAANaVcAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4a1KuK2qXVX1yaq6ZpqeOrWfUlUfrqrrq+qjVfXguXVWvQYAAMDWtJpXbp/S3adN02VT2yuTXNrdD0zykiSvmlt+LWoAAABsQWt2W3JVHZ3koUneMDW9PclJVXXiWtTWahwAAAAc/lYz3L6xqq6tqj+sqqOSHJfk8929L0m6u5PsTnL8GtXuoqp2VtWehWnv3r2rOFQAAAAOJ6sVbh/Z3T+R2VXVW5O8dmrvRcvV3Ou1qN25UPfF3b1jYdq+ffsBOw8AAMDYtq3GRrp79/Tz21X1u0muT/K5JDuqalt376uqyuzK6+4kt61BDQAAgC1qxVduq+peVfUDc01PS3J1d38pydVJnj61n51kV3fvWovaSscBAADAuFbjyu0xSd5eVXfP7BbhG5M8c6o9J8lrquoFSb6W5Ny59daiBgAAwBa04nDb3Tcm+ckD1D6V5BHrVQMAAGBrWrOvAgIAAID1ItwCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADG/F4baq7lFVl1fV9VV1TVW9t6pOnGpXVtWNU/s1VfX8ufWOnpa9oaquq6ozV1oDAABga1qtK7eXJnlQd5+W5IppfsH53X3aNL1srv3FSa7q7lOSPCvJG6tq2wprAAAAbEErDrfd/c3ufnd399R0VZKTl7DqOUkumbbxsSQ3JzlzhTUAAAC2oLX4zO35Sd45N39RVV1bVZdV1clJUlVHJrlbd98yt9yuJMcvt7b6wwAAAGAUqxpuq+oFSU5J8sKp6Rnd/aNJTk3ywcxuWV7Qi1dfhdp8X3ZW1Z6Fae/evUsZAgAAAANatXBbVRckOSvJ47r7tiTp7s9NP7u7X5Hk5Ko6srtvndY5am4TJyTZvdza4v5098XdvWNh2r59+2oNFQAAgMPMqoTbqtqZ5GlJHtPdX53atlXVMXPLnJ3k5oWAmuStSc6baqcnOTbJh1ZYAwAAYAta8VOGq2pHkpcmuTHJ+6sqSb6V5GeTvKuqjkhyR5IvJ3ni3KoXJnl9Vd2Q5PbMbmHet8IaAAAAW9CKw21378kBPvea5GEHWe/mJI9dzRoAAABb01o8LRkAAADWlXALAADA8IRbAAAAhifcAgAAMDzhFgAAgOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAAhifcAgAAMDzhFgAAgOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAAhifcAgAAMDzhFgAAgOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAAhifcAgAAMLwhw21VnVJVH66q66vqo1X14I3uEwAAABtnyHCb5JVJLu3uByZ5SZJXbXB/AAAA2EDDhduqOjrJQ5O8YWp6e5KTqurEjeoTAAAAG2u4cJvkuCSf7+59SdLdnWR3kuM3tFcAAABsmG0b3YFl6kXztXiBqtqZZOdc099X1RfXtFfLsz3J3o3uxDrZSmNNttZ4jfVQPOc5q9OT9eHYbk7GunltpfEa6+a0lcaabK3xHrvWO6jZhc9xTLcl35DkyO7eV1WV5AtJzujuXRvauWWoqj3dvWOj+7EettJYk601XmPdvLbSeI11c9pKY0221niNdXPaSmNNttZ412Osw92W3N1fSnJ1kqdPTWcn2TVisAUAAGB1jHpb8nOSvKaqXpDka0nO3eD+AAAAsIGGDLfd/akkj9jofqySize6A+toK4012VrjNdbNayuN11g3p6001mRrjddYN6etNNZka413zcc63GduAQAAYLHhPnMLAAAAiwm3AAAADE+43SBVdUpVfbiqrq+qj1bVgze6T4eiqu5RVZdP/b+mqt5bVSdOtaOn+Ruq6rqqOnNuvWXVDhdV9aKq6qp6yDR/wOO43NrhoKqOqKpXTMfiE1X1hql90423qv5pVf1FVV09ve/OndqHfx9X1curatf8e3ZqX/XjuNHHeH9jPdh5aqoPe4wPdGzn6i9aXNtMx3Zq3+95aiXjOYzHut/z1FQb8n18sH+fazGmjRzvdxnrq6vqU1P7B6rqtLn1vq+q3lRVn57WPWultY0e79wy507v8yfMtW22Y1tV9VtT7bqqunITj/VhVfWRmp2j/rqqfn1uvfV9H3e3aQOmJP8ryS9Pr5+S5CMb3adD7P89kjw+d35u+9eS/NH0+tVJfmt6fXqSzybZtpLa4TAleWiS90z9esh3O47LrR0OU5KXJXn53PH9oc043iSV5NYkp07zJyb5ZpJ/tBnex0kemWRHkl0L79m1Oo4bfYz3N9Yc5Dy1kuN4OBzjAx3bqfYd56rNdmyn9v2epzbbWHOQ89TI7+OD/ftcizFt5Hi/y1ifONfHJyS5fm69/5jkNdPrk5J8MckPrqS20eOd5nck+XCSjyR5wlz7Zju2z0vy9iTfO83Pn6M221ivTvLE6fV9knwpyYM34n28Lm9y03e8OY5O8tW5N2pNB+zEje7bCsb0sCSfnl7vTXLUXO2jSR69ktpGT0mOmE7CJ2X6o+Ngx3G5tY0e59Sfe039277U9+2o482dfzQ+cpo/NclNSb53M72Pc9c/lFf9OB5Oxzj7CXtztX84T63kOB5Ox3jxeLOfc9VaHfcNfh/v9zy1Scd6wPPUZnkfT/tf078jDqfxZtG5aK79vkm+leRu0/wnkpw+V39L7vyfL8uqHQ7jTfLuJA9PcmXuGm431bFNsifJAw6w3GYb69VJnjm9Pm4a+7Eb8T52W/LGOC7J57t7X5L07IjtTnL8hvZqZc5P8s6qOjKzk/Itc7VdSY5fbm1Ne710/ynJG7r7M3NtBzuOy60dDu6f2R9Sv1lVf15VH6yqn8smHO/Ul3OS/Leq+mySD2X2vdn/KJvzfZyszXE8bI/xIucneWeSbLFzVbL5ju2BzlPJJhvrgc5T3X37Jnsfr9nfEYfheP/hXLTI85K8u7vvmOaPz+zK3IJdubPPy61thPlz7/+Z5BPd/WfzC2y2Y1tV35/kqCRPrqqrpumpyeYb6/T6WUl+u6p2J7k+yb/v7i9OtXV9Hw/5PbebRC+arw3pxSqoqhckOSXJc5PcMwcf23JrG6aqHpHZrR+/sZ/yphrr5HuSnJzkr7r7N6rqJ5K8L7PbpTbVeKtqW5J/n+RJ3f2nVXV6ksszuzKyqca6yFqM7bAe96Lz1IJNNdbvcq5KNtd493ueqjs/I7tpxnqg81RV/fi0yPBjXae/Iw6L8R7gXJSqenpm/xPjpxetMt/vxX1ebm3dzI+3qk5K8q+S/B8HWHwzHdt7ZnYX2D27+4yqOj7JR6rqE0m+kM011iT5d0n+XXe/papOTnJlVX20uz811dftfezK7cb4XJId03+wUlWV2f8x3r2hvVqGqrogyVlJHtfdt3X3rVP7UXOLnZBk93Jra9n/JXpUkh9J8pmq2pXZZ0X+Z2a3Jh/oOB7sGB/ux/+zSe5I8sYk6e6/TPKZzI7HZhvvaUl+uLv/NEm6+2NJPp9ZuN1s7+MFyz1Wox7j7zhPJclWOldV1eOy+Y7tgc5TP5bNN9YDnad+YjO8j9fj74jDZbz7OxdN7U9N8qIkj+nuL82tsjuzW+YXzPd5ubV1s5/xPiLJDyf56+kcdUaSV1XVv9psx3bq194kb0iS7t6d5E+TPGyzjbWq7pvkyd39liTp7huT/FmSn5pWWd/3cW/APdqmTmafM/jl6fVTkly10X1axhh2JvmLLPpwd5LX5K4fdt+dOz/DtKza4TTlrp+FOuBxXG7tcJiS/FGSx0+vT0hyS5If2mzjTXJMkq8ledA0/4AkX0lyv830Pp5/z67V+/ZwOcb7Get+z1MrOY6H0zFePN71Pu4bfGz3e57abGPNQc5To7+PD/Tvcy3GtNHjPchYz0lyQ5IT9rPOb+WuD9S5Ocl9VlLb6PEuWubK3PUzt5vt2F6a5F9Pr38ws3/XD91sY01y98zOSY+a5u+b2f8sPH0j3sfr9iY3fceb40GZPfTj+iR/nuTHNrpPh9j/HZndKvA3Sa6Zpj+basdk9kfHDZl9GPxRc+stq3Y4TbnrHx0HPI7LrR0OU2a3+12Z5Nrp2D55s443ydOmcf5lko8n+aXN8j5OcklmD3XYl9mDcRYe/LDqx3Gjj/H+xpqDnKdGP8YHOraLltmVu4bBTXNsp/b9nqc26Vj3e54a+X18sH+fazGmjRzvdxnrtzMLAtfMTUdOtXsluSyz89n1SZ4yt81l1TZ6vIuWuzJ3Dbeb7djeN7PPpF43Tc/ZxGP9+cxC718m+askz1vpe3W57+OFRzkDAADAsHzmFgAAgOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALwLqqqmum6a+qat/c/GVV9eiq+vON7uNSVNWVVfWEdd7nk6rqr6ff14/PtT937vf4laraMzf/M+vZx6WqqrOq6mFz8w+vqtdtZJ+A/7+9ew2xqgrDOP5/aCqDMkREEY1QKyudMTMvpaZiakJSQlCOZoLRhTIIpUBKuyhJUVRiFnnN0LQirCRjLBonJy+ZMyZhphVBBNGHVEhRefuw16HNdLSZOcV4mOcHA/usvdd637Xnw/DutfYZs/JW0dYJmJlZ+xIRAwAkXQrsKnxObaPaJqu2IakiIk62oMt9wBMRsSHfGBFLgaVpzJVk93XxfxTz/zIZqCP7f7JExHZge5tmZGZmZc0rt2ZmdrapkLREUoOkfU1W98ZLqpP0laTtkkYWGyCtqi6StFXSQUlLc+dWSnow9/l5SfPT8XxJayV9KOl7SeslXSPpU0mHJL3QJNTYFOuApOckKY3TLfXdIalR0lO5eD9KmivpM2BVkdz7SKpJ/fZIujW1vwyMABZJ2taSG5ru2dOStgCbJJ0vabOkXZK+kbRaUod07UxJmyStS+d2pAcRSOoraVv63eyV9GRqHyepXtLu1GdaLnYPSe+l+TRKmidpEjARmJvmOEPSWElf5vrdnWI0SvpAUvfW5mdmZu2Di1szMzvbXA0sj4gq4BVgAYCkXsA8YGJEXAtUA2slnXuacXoDo4B+wHhJw5oZf1Aa+4r08yxwM9AfmCrp8ty1VwE3AVXAaOD21L4KWBwRg4GBwGBJt+X6XQKMiYjqIvHfAtZHRGUab5mknhExi2yVc1ZEXN/MueRVAuMjYhxwArgzIgaleR0jWxUuGArMiYh+QC0wJ7U/BLwfEVUR0R94KbXvBIZHxECye/6MpK7p3FqgLiIq05yWRMRGYBOwICIGRMSKfKKSqoCFKd/KNO9XS8jPzMzaAW9LNjOzs83+iCi8d1sPzE7HE4A+QG1aIC3oCRwqMs66iDgF/ClpD1mxW9+M+Jsj4g8ASY1AQ0QcB45L2g/0Ar5L166KiBPACUlryFZyPwLGAF1zeV4I9M3FWBER0TSwpIuAAcAygIg4IKkOGE5WJJbizdx2ZAGzJU0EzgEuBs7LXVsbET+n43rgnkI7sFBSR+BzYEtq7wIsl3QZcBLoDFwp6RjZw4IbCwNHxG/NyHUMsDEifkmflwA/lJCfmZm1Ay5uzczsbHMsd3yKv/9WCfg4Iu4qcZyTZAVdQQfg6Bn6nW6cYoJsV1QA16XCt5ijp2kvVMNNC99/FMKtkI85DRgGjIiII5IeAQbnzhedc0S8LWkrMBZ4mGyldBLwOrABmBwRkR4KdCghV3HmObc0PzMzawe8LdnMzMrFJ8AESf0KDZIGn+H60zkIDEn9O5O9+9la0yRVSLoAmALURMQRYCvwWC7P7pJ6/NtgEXEY2ANMT/16AzcAX5SQYzGdgN9TYdsRaNYDg7Qy+2tErCab39DceD+lwnY02VZw0gr4DrJCszBGl3R4mGzFuJgtwC25rc33AjUl5GdmZu2Ai1szMysLEXEAmAq8kb4w6FtyRVMLvAZ0k7SXbPtvKd/Qu5us6Gok2wb7TmqvJtuWuzfFeZdsq25zVJO929uQ+s3MbcH9r6wEOknal3KubWa/O4C9aZv3GuCB1P4o8KKkerL8d+b6TAFGpi9/agDuT+2rgemSvpY0Ix8kIhqAx4GatAo8JBerNfmZmVk7oCKv/JiZmZmZmZmVFa/cmpmZmZmZWdlzcWtmZmZmZmZlz8WtmZmZmZmZlT0Xt2ZmZmZmZlb2XNyamZmZmZlZ2XNxa2ZmZmZmZmXPxa2ZmZmZmZmVPRe3ZmZmZmZmVvZc3JqZmZmZmVnZ+wtq9kJr+dfd9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8), dpi=80)\n",
    "sns.distplot(df['Amount'], bins=3, kde=False, color='red')\n",
    "plt.xticks(np.arange(0, 30000, 2000))\n",
    "plt.yticks(np.arange(0, 350000, 25000))\n",
    "plt.xlabel('The number of Transactions')\n",
    "plt.title('Transaction Amounts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Transaction Amounts')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1105x425 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,5), dpi=85)\n",
    "sns.boxplot(df['Amount'])\n",
    "plt.title('Transaction Amounts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have used boxplot to see outliers more clearly. The amounts of the transactions seem right-skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "LABELS = [\"Normal\", \"Fraud\"]\n",
    "count_classes = pd.value_counts(df['Class'], sort = True)\n",
    "count_classes.plot(kind = 'bar', rot=30)\n",
    "plt.title(\"Transaction Class Distribution\")\n",
    "plt.xticks(range(2), LABELS)\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Fraud = df[df['Class']==1]\n",
    "\n",
    "Normal = df[df['Class']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((492, 31), (284315, 31))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fraud.shape, Normal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1368x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(19, 12))\n",
    "sns.heatmap(df.corr(), annot=True, linewidths=.3, fmt= '.2f',ax=ax, cmap='Blues',\n",
    "            xticklabels=df.columns, yticklabels=df.columns, cbar=True)\n",
    "plt.savefig(\"Correlation.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(10,10))\n",
    "\n",
    "amount = df['Amount'].values\n",
    "time = df['Time'].values\n",
    "\n",
    "sns.distplot(amount, ax=ax[0], color='y')\n",
    "ax[0].set_title('Distribution of Transaction Amount', fontsize=14)\n",
    "ax[0].set_xlim([min(amount), max(amount)])\n",
    "\n",
    "sns.distplot(time, ax=ax[1], color='yellow',rug=True, rug_kws={\"color\": \"g\"},\n",
    "                  kde_kws={\"color\": \"b\", \"lw\": 3, \"label\": \"KDE\"},\n",
    "                  hist_kws={\"histtype\": \"stepfilled\", \"linewidth\": 3,\n",
    "                            \"alpha\": 1, \"color\": \"r\"})\n",
    "ax[1].set_title('Distribution of Transaction Time', fontsize=14)\n",
    "ax[1].set_xlim([min(time), max(time)])\n",
    "\n",
    "plt.savefig(\"dis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(20,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17304750013189596"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_fraction = len(Fraud)/float(len(Normal))\n",
    "outlier_fraction*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This outlier fraction shows that we have a very imbalanced dataset. Our target (Fraud, Class==1) is only %17 of the dataset. The main concern here is that all ML models are designed to maximize the accuracy. Therefore, detecting all transaction as Normal will also give huge accuracy score, which is not helpful.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling & Distributing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I chose the RobustScaler scaler which is less prone to outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>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>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>scaled_time</th>\n",
       "      <th>scaled_amount</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>0.277838</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>-0.994983</td>\n",
       "      <td>1.783274</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>-0.638672</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>-0.994983</td>\n",
       "      <td>-0.269825</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>0.771679</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>-0.994972</td>\n",
       "      <td>4.983721</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>0.005274</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>-0.994972</td>\n",
       "      <td>1.418291</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>0.798278</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>-0.994960</td>\n",
       "      <td>0.670579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 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  ...       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  0.090794  ...  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024 -0.054952  ...  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  0.753074  ...  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Class  scaled_time  scaled_amount  \n",
       "0 -0.189115  0.133558 -0.021053      0    -0.994983       1.783274  \n",
       "1  0.125895 -0.008983  0.014724      0    -0.994983      -0.269825  \n",
       "2 -0.139097 -0.055353 -0.059752      0    -0.994972       4.983721  \n",
       "3 -0.221929  0.062723  0.061458      0    -0.994972       1.418291  \n",
       "4  0.502292  0.219422  0.215153      0    -0.994960       0.670579  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "robust_scaler = RobustScaler()\n",
    "\n",
    "df['scaled_time'] = robust_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n",
    "df['scaled_amount'] = robust_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "\n",
    "df.drop(['Time','Amount'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>scaled_amount</th>\n",
       "      <th>scaled_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>...</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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.783274</td>\n",
       "      <td>-0.994983</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>...</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.269825</td>\n",
       "      <td>-0.994983</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>...</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.983721</td>\n",
       "      <td>-0.994972</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>...</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.418291</td>\n",
       "      <td>-0.994972</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>...</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.670579</td>\n",
       "      <td>-0.994960</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>...</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "0       1.783274    -0.994983 -1.359807 -0.072781  2.536347  1.378155   \n",
       "1      -0.269825    -0.994983  1.191857  0.266151  0.166480  0.448154   \n",
       "2       4.983721    -0.994972 -1.358354 -1.340163  1.773209  0.379780   \n",
       "3       1.418291    -0.994972 -0.966272 -0.185226  1.792993 -0.863291   \n",
       "4       0.670579    -0.994960 -1.158233  0.877737  1.548718  0.403034   \n",
       "\n",
       "         V5        V6        V7        V8  ...       V20       V21       V22  \\\n",
       "0 -0.338321  0.462388  0.239599  0.098698  ...  0.251412 -0.018307  0.277838   \n",
       "1  0.060018 -0.082361 -0.078803  0.085102  ... -0.069083 -0.225775 -0.638672   \n",
       "2 -0.503198  1.800499  0.791461  0.247676  ...  0.524980  0.247998  0.771679   \n",
       "3 -0.010309  1.247203  0.237609  0.377436  ... -0.208038 -0.108300  0.005274   \n",
       "4 -0.407193  0.095921  0.592941 -0.270533  ...  0.408542 -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",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_amount = df['scaled_amount']\n",
    "scaled_time = df['scaled_time']\n",
    "\n",
    "df.drop(['scaled_amount', 'scaled_time'], axis=1, inplace=True)\n",
    "df.insert(0, 'scaled_amount', scaled_amount)\n",
    "df.insert(1, 'scaled_time', scaled_time)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Class'], axis=1)\n",
    "y = df.Class.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    227454\n",
       "1.0       391\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    284315\n",
       "1.0       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "Fraud = df[df['Class']==1]\n",
    "Normal = df[df['Class']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((492, 31), (284315, 31))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fraud.shape, Normal.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = LogisticRegression()\n",
    "LR.fit(X_train, y_train)\n",
    "y_pred = LR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     56861\n",
      "         1.0       0.88      0.63      0.74       101\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.94      0.82      0.87     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9994557775359011"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF = RandomForestClassifier(n_estimators=10).fit(X_train, y_train)\n",
    "RF_pred = RF.predict(X_test)\n",
    "\n",
    "accuracy_score(y_test, RF_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00     56861\n",
      "         1.0       0.93      0.75      0.83       101\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.96      0.88      0.92     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, RF_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Undersampling Majority Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will use sampling module from scikit-learn to remove some samples from the majority class 'Normal'. Undersampling can be a good method in case having a large dataset. One of the disadvantage here is loosing some information that might be valuable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    492\n",
      "0    492\n",
      "Name: Class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "undersampled_data = Normal.sample(len(Fraud))\n",
    "undersampled_data = pd.concat([undersampled_data, Fraud], axis=0)\n",
    "print(undersampled_data.Class.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_under = undersampled_data.drop('Class', axis=1)\n",
    "y_under = undersampled_data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_under, X_test_under, y_train_under, y_test_under = train_test_split(X_under, y_under, test_size=0.2, \n",
    "                                                                            random_state=0, stratify=y_under)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    394\n",
       "0    393\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_under.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have used 'liblinear' as a solver in LogisticRegression because it is better for small datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "undersample_LR = LogisticRegression(solver='liblinear')\n",
    "undersample_LR.fit(X_train_under, y_train_under)\n",
    "undersample_pred = undersample_LR.predict(X_test_under)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8984771573604061"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test_under, undersample_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8936170212765957"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test_under, undersample_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8571428571428571"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test_under, undersample_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1\n",
       "0  93   6\n",
       "1  14  84"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test_under, undersample_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.94      0.90        99\n",
      "           1       0.93      0.86      0.89        98\n",
      "\n",
      "    accuracy                           0.90       197\n",
      "   macro avg       0.90      0.90      0.90       197\n",
      "weighted avg       0.90      0.90      0.90       197\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test_under, undersample_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Oversampling Minority Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oversampling is basically used for adding some copies of minority class to have similar number of majority class samples.  I will again use sampling module from scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    284315\n",
      "0    284315\n",
      "Name: Class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "oversampled_data = Fraud.sample(len(Normal), replace = True)\n",
    "oversampled_data = pd.concat([oversampled_data, Normal], axis=0)\n",
    "print(oversampled_data.Class.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_over = oversampled_data.drop('Class', axis=1)\n",
    "y_over = oversampled_data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_over, X_test_over, y_train_over, y_test_over = train_test_split(X_over, y_over, test_size=0.2, random_state=0, stratify=y_over)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    227452\n",
       "0    227452\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_over.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_LR = LogisticRegression()\n",
    "oversample_LR.fit(X_train_over, y_train_over)\n",
    "oversample_pred = oversample_LR.predict(X_test_over)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9508115998100698"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test_over, oversample_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55588</td>\n",
       "      <td>1275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4319</td>\n",
       "      <td>52544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1\n",
       "0  55588   1275\n",
       "1   4319  52544"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test_over, oversample_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.98      0.95     56863\n",
      "           1       0.98      0.92      0.95     56863\n",
      "\n",
      "    accuracy                           0.95    113726\n",
      "   macro avg       0.95      0.95      0.95    113726\n",
      "weighted avg       0.95      0.95      0.95    113726\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test_over, oversample_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. SMOTE (Synthetic Minority Oversampling Technique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    284315\n",
       "1.0       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    284315\n",
       "0.0    284315\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smote = SMOTE(sampling_strategy='minority', random_state = 0)\n",
    "X_smote, y_smote = smote.fit_sample(X,y)\n",
    "\n",
    "y_smote.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_smote, X_test_smote, y_train_smote, y_test_smote = train_test_split(X_smote, y_smote, test_size=0.2, \n",
    "                                                                            random_state=0, stratify=y_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if we have same number of samples for each classes after using stratify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    56863\n",
       "0.0    56863\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_smote.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote_LR = LogisticRegression()\n",
    "smote_LR.fit(X_train_smote, y_train_smote)\n",
    "smote_y_pred = smote_LR.predict(X_test_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9478395441675607"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test_smote, smote_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55483</td>\n",
       "      <td>1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4552</td>\n",
       "      <td>52311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1\n",
       "0  55483   1380\n",
       "1   4552  52311"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test_smote, smote_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.98      0.95     56863\n",
      "         1.0       0.97      0.92      0.95     56863\n",
      "\n",
      "    accuracy                           0.95    113726\n",
      "   macro avg       0.95      0.95      0.95    113726\n",
      "weighted avg       0.95      0.95      0.95    113726\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test_smote, smote_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Fraud Detection</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>under_sampling</th>\n",
       "      <td>0.898477</td>\n",
       "      <td>0.893617</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>over_sampling</th>\n",
       "      <td>0.950812</td>\n",
       "      <td>0.949459</td>\n",
       "      <td>0.924046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMOTE</th>\n",
       "      <td>0.947840</td>\n",
       "      <td>0.946343</td>\n",
       "      <td>0.919948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Fraud Detection  Accuracy  F1-score    Recall\n",
       "under_sampling   0.898477  0.893617  0.857143\n",
       "over_sampling    0.950812  0.949459  0.924046\n",
       "SMOTE            0.947840  0.946343  0.919948"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "under_accuracy = accuracy_score(y_test_under, undersample_pred)\n",
    "over_accuracy = accuracy_score(y_test_over, oversample_pred)\n",
    "smote_accuracy = accuracy_score(y_test_smote, smote_y_pred)\n",
    "\n",
    "under_f1_score = f1_score(y_test_under, undersample_pred)\n",
    "over_f1_score = f1_score(y_test_over, oversample_pred)\n",
    "smote_f1_score = f1_score(y_test_smote, smote_y_pred)\n",
    "\n",
    "under_recall_score = recall_score(y_test_under, undersample_pred)\n",
    "over_recall_score = recall_score(y_test_over, oversample_pred)\n",
    "smote_recall_score = recall_score(y_test_smote, smote_y_pred)\n",
    "\n",
    "list_accuracy = [under_accuracy, over_accuracy, smote_accuracy]\n",
    "list_f1_score = [under_f1_score, over_f1_score, smote_f1_score]\n",
    "list_recall = [under_recall_score, over_recall_score, smote_recall_score]\n",
    "                 \n",
    "dataframe = pd.DataFrame(list_accuracy, index=['under_sampling','over_sampling','SMOTE'])\n",
    "dataframe.columns = ['Accuracy']\n",
    "dataframe.insert(loc=1, column='F1-score', value=list_f1_score)\n",
    "dataframe.insert(loc=2, column='Recall', value=list_recall)\n",
    "dataframe.columns.name = 'Fraud Detection'\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I have covered some methods and algorithms to deal with the imbalanced data. \n",
    "Of course, there are several different methods to collect data with different methods. Lets try to solve the problem using ANN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply ANN "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- First create an ANN and visualize it using ROC & AUC metrics.\n",
    "- Then, use weights handling technique to make the data balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,1:30]\n",
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    213233\n",
       "1       372\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weighted Neural Network using Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Define weights & fit the data with these specific weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_assigned={0:1,1:555}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_inputs = len(X_train.columns)\n",
    "n_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "6676/6676 [==============================] - 10s 2ms/step - loss: 0.6423 - accuracy: 0.9731\n",
      "Epoch 2/15\n",
      "6676/6676 [==============================] - 12s 2ms/step - loss: 0.2707 - accuracy: 0.9722\n",
      "Epoch 3/15\n",
      "6676/6676 [==============================] - 16s 2ms/step - loss: 0.2262 - accuracy: 0.9730\n",
      "Epoch 4/15\n",
      "6676/6676 [==============================] - 12s 2ms/step - loss: 0.1747 - accuracy: 0.9784\n",
      "Epoch 5/15\n",
      "6676/6676 [==============================] - 11s 2ms/step - loss: 0.2088 - accuracy: 0.9767\n",
      "Epoch 6/15\n",
      "6676/6676 [==============================] - 11s 2ms/step - loss: 0.1486 - accuracy: 0.9788\n",
      "Epoch 7/15\n",
      "6676/6676 [==============================] - 9s 1ms/step - loss: 0.1535 - accuracy: 0.9791\n",
      "Epoch 8/15\n",
      "6676/6676 [==============================] - 8s 1ms/step - loss: 0.1266 - accuracy: 0.9820\n",
      "Epoch 9/15\n",
      "6676/6676 [==============================] - 9s 1ms/step - loss: 0.1270 - accuracy: 0.9797: 0s -\n",
      "Epoch 10/15\n",
      "6676/6676 [==============================] - 8s 1ms/step - loss: 0.0948 - accuracy: 0.9859\n",
      "Epoch 11/15\n",
      "6676/6676 [==============================] - 8s 1ms/step - loss: 0.1254 - accuracy: 0.9820\n",
      "Epoch 12/15\n",
      "6676/6676 [==============================] - 8s 1ms/step - loss: 0.1311 - accuracy: 0.9832\n",
      "Epoch 13/15\n",
      "6676/6676 [==============================] - 8s 1ms/step - loss: 0.0997 - accuracy: 0.9849\n",
      "Epoch 14/15\n",
      "6676/6676 [==============================] - 8s 1ms/step - loss: 0.1166 - accuracy: 0.9851\n",
      "Epoch 15/15\n",
      "6676/6676 [==============================] - 9s 1ms/step - loss: 0.0834 - accuracy: 0.9881\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x11d5cd350>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(Dense(50, input_dim=n_inputs, activation='relu', kernel_initializer='he_uniform'))\n",
    "model.add(Dense(15, input_dim=n_inputs, activation='relu', kernel_initializer='he_uniform'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "model.fit(X_train, y_train, class_weight=weights_assigned, epochs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2226/2226 [==============================] - 2s 814us/step - loss: 0.0451 - accuracy: 0.9881\n",
      "[0.04509672522544861, 0.9881182909011841]\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99     71082\n",
      "           1       0.11      0.90      0.20       120\n",
      "\n",
      "    accuracy                           0.99     71202\n",
      "   macro avg       0.56      0.94      0.60     71202\n",
      "weighted avg       1.00      0.99      0.99     71202\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model.evaluate(X_test, y_test))\n",
    "    \n",
    "y_preds = model.predict(X_test)\n",
    "y_preds = np.round(y_preds)\n",
    "    \n",
    "print(\"Classification Report: \\n\", classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9441335359162658"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### THE END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}