1-1. Banking-fraud-prepare-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1: Prepare Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents\n",
    "1. [Introduction](#Introduction)\n",
    "2. [Prerequisites and Preprocessing](#Prequisites-and-Preprocessing)\n",
    "3. [Data Preprocessing](#Data-Preprocessing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "은행거래에서의 사기를 판별하는 것은 실제 은행에서 매우 중요한 일이며 매우 소수가 사기(Fraud)이지만 만약 그 거래가 잘 탐지되지 않았을 때 피해는 매우 큽니다. 본 notebook을 통해 가상의 금융 거래 데이터로 데이터 분석 및 이진 분류 모델을 (binary classifier) 만듭니다. 데이터는 [kaggle](https://www.kaggle.com/)에서도 받으실 수 있고, 여기서 사용된 데이터는 이미 가공을 한 번 거친 상태이기에 원본 데이터를 사용하신다면 column이나 row 수가 다를 수 있습니다.\n",
    "\n",
    "\n",
    "### About Data\n",
    "\n",
    "이 데이터셋은 금융 응용프로그램의 연구를 돕기 위한 시뮬레이션 데이터셋으로, 9개의 컬럼으로 3000개의 레코드를 갖습니다. 각 레코드는 하나의 거래를 의미하며 입금(cash in), 출금(cash out), 직불(debit), 신용(credit), 또는 송금(transfer)를 나타내고, 각 거래는 금액과 거래주체(name of origin) 등을 가집니다. 사기여부를 나타내는 ``isFraud``가 label 혹은 target column이 됩니다.\n",
    "\n",
    "- type : 거래 유형 (CASH-IN, CASH-OUT, DEBIT, PAYMENT, TRANSFER)\n",
    "- amount : 거래 금액\n",
    "- nameOrig : 거래를 시작한 주체\n",
    "- oldbalanceOrg : 거래 시작 주체의 거래 이전 잔고\n",
    "- newbalanceOrig : 거래 시작 주체의 거래 이후 잔고\n",
    "- nameDest : 거래 대상 주체\n",
    "- oldbalanceDest : 거래 대상의 거래 이전 잔고\n",
    "- newbalanceDest : 거래 대상의 거래 이후 잔고\n",
    "- isFraud : 사기 여부"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites and Preprocessing\n",
    "### Import libraries\n",
    "\n",
    "Notebook을 진행하기 위해 여러 라이브러리를 다운로드 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                         # For manipulating filepath names  \n",
    "import sys                                        # For writing outputs to notebook\n",
    "\n",
    "import numpy as np                                # For matrix operations and numerical processing\n",
    "import pandas as pd                               # For munging tabular data\n",
    "\n",
    "import matplotlib.pyplot as plt                   # For charts and visualizations\n",
    "from IPython.display import Image                 # For displaying images in the notebook\n",
    "from IPython.display import display               # For displaying outputs in the notebook\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "import sagemaker                                  # Amazon SageMaker's Python SDK provides many helper functions\n",
    "import boto3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Permissions and environment variables\n",
    "\n",
    "SageMaker가 AWS 계정의 리소스에 액세스 할 수 있도록 노트북 환경에서 IAM (Identity and Access Management) 실행 역할을 가져옵니다. 본 notebook에서는 데이터 훈련 및 호스팅 액세스 권한을 부여하는데 사용되는 역할이 필요합니다. ``get_execution_role`` 이 role을 적절한 권한이 있는 역할을 반환하지 않으면 IAM 역할을 따로 지정해야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sagemaker_session = sagemaker.Session()\n",
    "s3 = boto3.resource('s3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set S3 bucket\n",
    "\n",
    "데이터를 불러오고 저장할 ``S3 bucket의 이름`` 을 설정합니다.\n",
    "\n",
    "---\n",
    "### 아래 값을 변경해주세요.\n",
    "\n",
    "여러분의 S3 bucket 명으로 변경합니다. 예) 'sagemaker-workshop-성함'\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bucket = 'CHANGE TO YOUR S3 BUCKET NAME'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'banking_fraud.csv'                         # 불러올 파일의 이름입니다    \n",
    "prefix = 'banking-fraud'                           # S3 bucket에서의 폴더명이라고 생각하시면 됩니다  \n",
    "\n",
    "data_location = 's3://{}/{}'.format(bucket, file) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'bucket' (str)\n",
      "Stored 'prefix' (str)\n",
      "Stored 'data_location' (str)\n"
     ]
    }
   ],
   "source": [
    "%store bucket\n",
    "%store prefix\n",
    "%store data_location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "### Read dataset\n",
    "\n",
    "위의 셀에서 설정한 S3 bucket 위치에서 데이터를 불러옵니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(data_location)\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>140421.18</td>\n",
       "      <td>C1667570766</td>\n",
       "      <td>16004.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C2102410298</td>\n",
       "      <td>0.00</td>\n",
       "      <td>140421.18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>216666.53</td>\n",
       "      <td>C1495945377</td>\n",
       "      <td>50398.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C814408370</td>\n",
       "      <td>10119297.16</td>\n",
       "      <td>10335963.70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>234636.20</td>\n",
       "      <td>C269129885</td>\n",
       "      <td>74262.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1389815469</td>\n",
       "      <td>166046.48</td>\n",
       "      <td>400682.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>52816.29</td>\n",
       "      <td>C129678616</td>\n",
       "      <td>117751.0</td>\n",
       "      <td>170567.29</td>\n",
       "      <td>C842027837</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>63871.25</td>\n",
       "      <td>C1282823885</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1236511065</td>\n",
       "      <td>456488.36</td>\n",
       "      <td>520359.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type     amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
       "0  CASH_OUT  140421.18  C1667570766        16004.0            0.00   \n",
       "1  CASH_OUT  216666.53  C1495945377        50398.0            0.00   \n",
       "2  CASH_OUT  234636.20   C269129885        74262.0            0.00   \n",
       "3   CASH_IN   52816.29   C129678616       117751.0       170567.29   \n",
       "4  CASH_OUT   63871.25  C1282823885         6012.0            0.00   \n",
       "\n",
       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  \n",
       "0  C2102410298            0.00       140421.18        0  \n",
       "1   C814408370     10119297.16     10335963.70        0  \n",
       "2  C1389815469       166046.48       400682.68        0  \n",
       "3   C842027837            0.00            0.00        0  \n",
       "4  C1236511065       456488.36       520359.60        0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``isFraud``를 예측하는 모델을 만들 것이기에 isFraud 가 1 (True) 인 값이 얼마나 많은지 확인합니다. 전체의 11.2%가 Fraud인 것을 알 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.112"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['isFraud'].mean() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA (Exploratory Data Analysis, 탐색적 데이터 분석)\n",
    "\n",
    "모델을 만들기 위해 데이터의 분포 및 평균, 어떤 feature가 있는지 등을 확인합니다. 각 feature에 대한 정보를 통해 feature engineering을 진행합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type 5\n",
      "nameOrig 3000\n",
      "nameDest 2999\n"
     ]
    }
   ],
   "source": [
    "# Frequency tables for each categorical feature\n",
    "for column in df.select_dtypes(include=['object']).columns:\n",
    "    print(column, len(df[column].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 categorical feature의 값을 확인할 수 있습니다.\n",
    "- type: 5개의 카테고리가 있으므로 one-hot-encoding 으로 변형\n",
    "- nameOrig, nameDest: 많은 카테고리가 존재하므로 삭제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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>col_0</th>\n",
       "      <th>% observations</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CASH_IN</th>\n",
       "      <td>0.144667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CASH_OUT</th>\n",
       "      <td>0.311667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEBIT</th>\n",
       "      <td>0.017000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYMENT</th>\n",
       "      <td>0.414667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSFER</th>\n",
       "      <td>0.112000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0     % observations\n",
       "type                    \n",
       "CASH_IN         0.144667\n",
       "CASH_OUT        0.311667\n",
       "DEBIT           0.017000\n",
       "PAYMENT         0.414667\n",
       "TRANSFER        0.112000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# type의 카테고리 확인\n",
    "column = 'type'\n",
    "display(pd.crosstab(index=df[column], columns='% observations', normalize='columns'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000e+03</td>\n",
       "      <td>3.000000e+03</td>\n",
       "      <td>3.000000e+03</td>\n",
       "      <td>3.000000e+03</td>\n",
       "      <td>3.000000e+03</td>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.542131e+05</td>\n",
       "      <td>3.209679e+05</td>\n",
       "      <td>1.460443e+05</td>\n",
       "      <td>6.789421e+05</td>\n",
       "      <td>8.044602e+05</td>\n",
       "      <td>0.112000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.072189e+06</td>\n",
       "      <td>2.255314e+06</td>\n",
       "      <td>1.592268e+06</td>\n",
       "      <td>3.156094e+06</td>\n",
       "      <td>3.243301e+06</td>\n",
       "      <td>0.315419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.515708e+03</td>\n",
       "      <td>1.035475e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.297213e+04</td>\n",
       "      <td>3.058350e+04</td>\n",
       "      <td>4.176675e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.522532e+05</td>\n",
       "      <td>9.948825e+04</td>\n",
       "      <td>6.888302e+04</td>\n",
       "      <td>2.255525e+05</td>\n",
       "      <td>3.795359e+05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>5.731626e+07</td>\n",
       "      <td>4.731626e+07</td>\n",
       "      <td>1.053767e+08</td>\n",
       "      <td>1.052426e+08</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "count  3.000000e+03   3.000000e+03    3.000000e+03    3.000000e+03   \n",
       "mean   2.542131e+05   3.209679e+05    1.460443e+05    6.789421e+05   \n",
       "std    1.072189e+06   2.255314e+06    1.592268e+06    3.156094e+06   \n",
       "min    0.000000e+00   0.000000e+00    0.000000e+00    0.000000e+00   \n",
       "25%    7.515708e+03   1.035475e+04    0.000000e+00    0.000000e+00   \n",
       "50%    3.297213e+04   3.058350e+04    4.176675e+03    0.000000e+00   \n",
       "75%    1.522532e+05   9.948825e+04    6.888302e+04    2.255525e+05   \n",
       "max    1.000000e+07   5.731626e+07    4.731626e+07    1.053767e+08   \n",
       "\n",
       "       newbalanceDest      isFraud  \n",
       "count    3.000000e+03  3000.000000  \n",
       "mean     8.044602e+05     0.112000  \n",
       "std      3.243301e+06     0.315419  \n",
       "min      0.000000e+00     0.000000  \n",
       "25%      0.000000e+00     0.000000  \n",
       "50%      0.000000e+00     0.000000  \n",
       "75%      3.795359e+05     0.000000  \n",
       "max      1.052426e+08     1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 히스토그램을 통해 numeric features 의 분포 확인\n",
    "display(df.describe())\n",
    "\n",
    "%matplotlib inline\n",
    "hist = df.hist(bins=30, sharey=True, figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation 확인\n",
    "\n",
    "각 feature 가 얼마나 유용할지 간접적으로 살펴보기 위하여 target variable 인 isFraud 와의 상관관계를 살펴봅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>isFraud</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CASH_IN</th>\n",
       "      <td>0.162913</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CASH_OUT</th>\n",
       "      <td>0.287913</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEBIT</th>\n",
       "      <td>0.019144</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAYMENT</th>\n",
       "      <td>0.466967</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSFER</th>\n",
       "      <td>0.063063</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "isFraud          0    1\n",
       "type                   \n",
       "CASH_IN   0.162913  0.0\n",
       "CASH_OUT  0.287913  0.5\n",
       "DEBIT     0.019144  0.0\n",
       "PAYMENT   0.466967  0.0\n",
       "TRANSFER  0.063063  0.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_column = 'isFraud'\n",
    "x_column = 'type'\n",
    "display(pd.crosstab(index=df[x_column], columns=df[y_column], normalize='columns'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amount\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oldbalanceOrg\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEgCAYAAABIJS/hAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcWklEQVR4nO3dcbRdZX3m8e9DoqggGhYhg0k06AQqZDToLaUy06UyU1K1hs4sukKrpDY1lqJCl50arDN2zTJdOFqsLoU2hUiwCI2KJaOCpqlKmQFigCiECESDcCUmUURQu9CEZ/7Yb+RwOTd333POPWefy/NZ666zz7vfvffvXnL4nf2+735f2SYiIqKOQwYdQEREDI8kjYiIqC1JIyIiakvSiIiI2pI0IiKitiSNiIioLUkjIiJqS9KYBiQdKelzkn4q6buSfm/QMUUMgqS3S9oi6TFJlw86nulo5qADiJ74OPBzYA6wGPiCpG/Y3jbQqCL670Hg/cDpwLMHHMu0pDwRPtwkHQb8CFhk+55S9knge7ZXDTS4iAGR9H5gnu0/GHQs002ap4bfccD+Awmj+AZw4oDiiYhpLElj+B0O/HhM2Y+B5w4gloiY5pI0ht9PgCPGlB0BPDqAWCJimkvSGH73ADMlLWwpezmQTvCI6LkkjSFn+6fANcD/knSYpFOBpcAnBxtZRP9JminpWcAMYIakZ0nKKNEeStKYHv6EanjhHuAq4JwMt42nqfcC/wasAt5Utt870IimmQy5jYiI2nKnERERtSVpREREbUkaERFRW5JGRETU1vihaEcddZQXLFgw6DBimrn11lt/YHv2oOOYjHwWYipM9rPQ+KSxYMECtmzZMugwYpqR9N1BxzBZ+SzEVJjsZyHNUxERUduESUPSWkl7JN05pvwdku6WtE3S/24pv0DSjrLv9JbyV0q6o+z7qCT19leJiIipVudO43JgSWuBpNdQTVXxMtsnAh8q5ScAy6im5V4CXCxpRjnsEmAlsLD8POmcERHRfBMmDds3AA+NKT4HuND2Y6XOnlK+FLja9mO2dwI7gJMlHQMcYfsmV4+gXwGc0aPfISIi+qTTPo3jgP8k6RZJX5P0q6V8LvBAS73RUja3bI8tj4iIIdLp6KmZwCzgFOBXgfWSXgy066fwQcrbkrSSqimLF77whR2GGBERvdbpncYocI0rm4HHgaNK+fyWevOoFnofLdtjy9uyvcb2iO2R2bOHaih9RMS01mnS+CfgtQCSjgOeCfwA2AAsk3SopGOpOrw3294FPCrplDJq6mzg2m6Dj4iI/pqweUrSVcCrgaMkjQLvA9YCa8sw3J8Dy0sH9zZJ64G7gH3Aubb3l1OdQzUS69nAdeUnIiKGyIRJw/ZZ4+x60zj1VwOr25RvARZNKroJLFj1hSe9v+/C1/fy9BFDI5+F6Jc8ER4REbUlaURERG1JGhERUVuSRkRE1JakERERtSVpREREbUkaERFRW5JGRETUlqQRERG1JWlERERtSRoRPSBpvqSvSNpelkA+r5T/paTvSdpafl7XckzbpZEjmqzT9TQi4sn2Ae+yfZuk5wK3StpY9n3Y9odaK49ZGvkFwD9LOq5lgs+IRsqdRkQP2N5l+7ay/SiwnYOvTtl2aeSpjzSiO0kaET0maQFwEnBLKXq7pG9KWitpVikbb2nksedaKWmLpC179+6dyrAjaknSiOghSYcDnwXOt/0IcAnwEmAxsAv46wNV2xz+lCWQs4plNE2SRkSPSHoGVcK40vY1ALZ3295v+3Hg73miCWq8pZEjGi1JI6IHyjLGlwHbbV/UUn5MS7XfAe4s222XRu5XvBGdyuipiN44FXgzcIekraXsPcBZkhZTNT3dB7wNwPbBlkaOaKw6a4SvBd4A7LG9aMy+PwM+CMy2/YNSdgGwAtgPvNP2l0r5K3lijfAvAueVdcUjhp7tG2nfT/HFgxzTdmnkiCar0zx1ObBkbKGk+cB/Ae5vKWsde74EuFjSjLL7EmAl1W34wnbnjIiIZpswadi+AXioza4PA3/Ok0d8tB17Xtp1j7B9U7m7uAI4o9vgIyKivzrqCJf0RuB7tr8xZtd4Y8/nlu2x5eOdP2PTIyIaaNJJQ9JzgL8A/me73W3KfJDytjI2PSKimToZPfUS4FjgG9UoQ+YBt0k6mfHHno+W7bHlERExRCZ9p2H7DttH215gewFVQniF7e8zzthz27uARyWdUsaznw1c27tfIyIi+mHCpCHpKuAm4HhJo5JWjFfX9jbgwNjz63ny2PNzgEupOse/DVzXZewREdFnEzZP2T5rgv0LxrxvO/bc9hZg0djyiIgYHplGJCIiakvSiIiI2pI0IiKitiSNiIioLUkjIiJqS9KIiIjakjQiIqK2JI2IiKgtSSMiImpL0oiIiNqSNCIiorYkjYiIqC1JIyIiakvSiIiI2pI0IiKitiSNiIioLUkjIiJqq7Pc61pJeyTd2VL2QUnfkvRNSZ+T9PyWfRdI2iHpbkmnt5S/UtIdZd9Hy1rhERExROrcaVwOLBlTthFYZPtlwD3ABQCSTgCWASeWYy6WNKMccwmwElhYfsaeMyIiGm7CpGH7BuChMWVftr2vvL0ZmFe2lwJX237M9k5gB3CypGOAI2zfZNvAFcAZPfodIiKiT3rRp/GHwHVley7wQMu+0VI2t2yPLW9L0kpJWyRt2bt3bw9CjIiIXugqaUj6C2AfcOWBojbVfJDytmyvsT1ie2T27NndhBjRF5LmS/qKpO2Stkk6r5QfKWmjpHvL66yWY9r2/0U0WcdJQ9Jy4A3A75cmJ6juIOa3VJsHPFjK57Upj5gu9gHvsv1S4BTg3NLHtwrYZHshsKm8n6j/L6KxOkoakpYA7wbeaPtnLbs2AMskHSrpWKoO7822dwGPSjqljJo6G7i2y9gjGsP2Ltu3le1Hge1UTbBLgXWl2jqe6Mtr2//X16AjOlBnyO1VwE3A8ZJGJa0APgY8F9goaaukvwWwvQ1YD9wFXA+ca3t/OdU5wKVUH45v80Q/SMS0ImkBcBJwCzCnfGmivB5dqo3X/zf2XOnfi0aZOVEF22e1Kb7sIPVXA6vblG8BFk0quoghI+lw4LPA+bYfOcjjSLX6+WyvAdYAjIyMjNsPGNEveSI8okckPYMqYVxp+5pSvLsMOae87inl4/X/RTRakkZED5S+usuA7bYvatm1AVhetpfzRF9e2/6/fsUb0akJm6ciopZTgTcDd0jaWsreA1wIrC99gfcDZ0LV/yfpQP/fPp7c/xfRWEkaET1g+0ba91MAnDbOMW37/yKaLM1TERFRW5JGRETUlqQRERG1JWlERERtSRoREVFbkkZERNSWpBEREbUlaURERG1JGhERUVuSRkRE1JakERERtSVpREREbUkaERFRW53lXtdK2iPpzpayIyVtlHRveZ3Vsu8CSTsk3S3p9JbyV0q6o+z7qA6ypFlERDRTnTuNy4ElY8pWAZtsLwQ2lfdIOgFYBpxYjrlY0oxyzCXASqrFZha2OWdERDTchEnD9g3AQ2OKlwLryvY64IyW8qttP2Z7J7ADOLksc3mE7ZtsG7ii5ZiIiBgSnfZpzLG9C6C8Hl3K5wIPtNQbLWVzy/bY8rYkrZS0RdKWvXv3dhhiRET0Wq87wtv1U/gg5W3ZXmN7xPbI7NmzexZcRER0p9Oksbs0OVFe95TyUWB+S715wIOlfF6b8oiIGCKdJo0NwPKyvRy4tqV8maRDJR1L1eG9uTRhPSrplDJq6uyWYyIiYkjMnKiCpKuAVwNHSRoF3gdcCKyXtAK4HzgTwPY2SeuBu4B9wLm295dTnUM1EuvZwHXlJyIihsiEScP2WePsOm2c+quB1W3KtwCLJhVdREQ0Sp4Ij4iI2pI0IiKitiSNiIioLUkjIiJqS9KIiIjakjQiIqK2JI2IiKgtSSOiB8ZZd+YvJX1P0tby87qWfW3XnYlouiSNiN64nPZrxHzY9uLy80WYcN2ZiEZL0ojogXHWnRlP23Vnpiy4iB5K0oiYWm+X9M3SfHVgWeTx1p2JaLwkjYipcwnwEmAxsAv461Jee32ZLEgWTZOkETFFbO+2vd/248Df80QT1HjrzrQ7RxYki0ZJ0oiYIgcWKit+BzgwsqrtujP9ji+iExNOjR4RExtn3ZlXS1pM1fR0H/A2mHDdmYhGS9KI6IFx1p257CD12647E9F0aZ6KiIjaukoakv5U0jZJd0q6StKzJB0paaOke8vrrJb6eQo2ImKIdZw0JM0F3gmM2F4EzKB6ynUVsMn2QmBTeZ+nYCMipoFum6dmAs+WNBN4DtWwwaXAurJ/HXBG2c5TsBERQ67jpGH7e8CHgPupHlz6se0vA3Ns7yp1dgFHl0NqPwWbB5oiIpqpm+apWVR3D8cCLwAOk/Smgx3SpqztU7B5oCkiopm6aZ76z8BO23tt/wK4BngVsPvAQ03ldU+pX/sp2IiIaKZuksb9wCmSniNJwGnAdqqnXZeXOsuBa8t2noKNiBhyHT/cZ/sWSZ8BbqN6qvV2YA1wOLBe0gqqxHJmqZ+nYCMihlxXT4Tbfh/VdAmtHqO662hXP0/BRkQMsTwRHhERtSVpREREbUkaERFRW5JGRETUlqQRERG1JWlERERtSRoREVFbkkZERNSWpBEREbUlaURERG1JGhERUVuSRkRE1JakERERtSVpREREbUkaERFRW5JGRETUlqQRERG1dZU0JD1f0mckfUvSdkm/LulISRsl3VteZ7XUv0DSDkl3Szq9+/AjIqKfur3T+Ahwve1fAV4ObAdWAZtsLwQ2lfdIOgFYBpwILAEuljSjy+tHREQfdZw0JB0B/AZwGYDtn9t+GFgKrCvV1gFnlO2lwNW2H7O9E9gBnNzp9SMiov+6udN4MbAX+ISk2yVdKukwYI7tXQDl9ehSfy7wQMvxo6UsYuhJWitpj6Q7W8rSVBvTTjdJYybwCuAS2ycBP6U0RY1DbcrctqK0UtIWSVv27t3bRYgRfXM5VbNrqzTVxrTTTdIYBUZt31Lef4YqieyWdAxAed3TUn9+y/HzgAfbndj2Gtsjtkdmz57dRYgR/WH7BuChMcVpqo1pp+OkYfv7wAOSji9FpwF3ARuA5aVsOXBt2d4ALJN0qKRjgYXA5k6vHzEEum6qzV13NM3MLo9/B3ClpGcC3wHeQpWI1ktaAdwPnAlge5uk9VSJZR9wru39XV4/YhjVbqq1vQZYAzAyMtK2TkQ/dZU0bG8FRtrsOm2c+quB1d1cM2KI7JZ0jO1dnTbVRjRNngiPmDppqo1pp9vmqYgAJF0FvBo4StIo8D7gQtJUG9NMkkZED9g+a5xdaaqNaSXNUxERUVuSRkRE1JakERERtSVpREREbUkaERFRW5JGRETUlqQRERG1JWlERERtSRoREVFbkkZERNSWpBEREbUlaURERG3TasLCBau+8JSy+y58/QAiiYiYnnKnERERtSVpREREbV0nDUkzJN0u6fPl/ZGSNkq6t7zOaql7gaQdku6WdHq3146IiP7qxZ3GecD2lvergE22FwKbynsknQAsA04ElgAXS5rRg+tHRESfdJU0JM0DXg9c2lK8FFhXttcBZ7SUX237Mds7gR3Ayd1cPyIi+qvbO42/Af4ceLylbI7tXQDl9ehSPhd4oKXeaCl7CkkrJW2RtGXv3r1dhhgREb3ScdKQ9AZgj+1b6x7SpsztKtpeY3vE9sjs2bM7DTEiInqsm+c0TgXeKOl1wLOAIyT9A7Bb0jG2d0k6BthT6o8C81uOnwc82MX1IyKizzq+07B9ge15thdQdXD/i+03ARuA5aXacuDasr0BWCbpUEnHAguBzR1HHhERfTcVT4RfCKyXtAK4HzgTwPY2SeuBu4B9wLm290/B9SMiYor0JGnY/irw1bL9Q+C0ceqtBlb34poREdF/eSI8IiJqS9KIiIjakjQiIqK2JI2IiKgtSSMiImqbVoswRTSRpPuAR4H9wD7bI5KOBP4RWADcB/yu7R8NKsaIunKnEdEfr7G92PZIed92NuiIpkvSiBiM8WaDjmi0JI2IqWfgy5JulbSylI03G/STZMbnaJr0aURMvVNtPyjpaGCjpG/VPdD2GmANwMjISNtZoSP6KXcaEVPM9oPldQ/wOarFx3aXWaAZMxt0RKMlaURMIUmHSXrugW3gN4E7GX826IhGS/NUxNSaA3xOElSft0/Zvl7S12kzG3RE0yVpREwh298BXt6mfNzZoCOaLM1TERFRW5JGRETUlqQRERG1dZw0JM2X9BVJ2yVtk3ReKT9S0kZJ95bXWS3HXCBph6S7JZ3ei18gIiL6p5s7jX3Au2y/FDgFOFfSCYwzp07Ztww4EVgCXCxpRjfBR0REf3WcNGzvsn1b2X4U2A7MZfw5dZYCV9t+zPZOYAfVQ04RETEketKnIWkBcBJwC+PPqTMXeKDlsNFS1u58mW8nIqKBuk4akg4HPgucb/uRg1VtU9Z2Lh3ba2yP2B6ZPXt2tyFGRESPdPVwn6RnUCWMK21fU4p3SzrG9q4xc+qMAvNbDp8HPNjN9TuxYNUXnlJ234Wv73cYERFDqZvRUwIuA7bbvqhl13hz6mwAlkk6VNKxwEJgc6fXj4iI/uvmTuNU4M3AHZK2lrL3ABfSZk4d29skrQfuohp5da7t/V1cPyIi+qzjpGH7Rtr3U8A4c+rYXg2s7vSanWjXHBUREZ3JE+EREVFbkkZERNSWpBEREbUlaURERG1JGhERUVuSRkRE1JakERERtWWN8IhpKNPlxFTJnUZERNSWpBEREbWleSriaWxsM1a7Jqw6deLpI3caERFRW5JGRETUluYpcoseEVFX7jQiIqK23GlEPE1kbZnohSSNNnr54UqzVkRMJ31PGpKWAB8BZgCX2r6w3zH0Qp64jW5Nl89CPL30tU9D0gzg48BvAScAZ0k6oZ8xRDRBPgsxrPp9p3EysMP2dwAkXQ0sBe7qcxxTok6zVp06nY7eSlPYUGnkZ2Eq+z3y73N66HfSmAs80PJ+FPi1sZUkrQRWlrc/kXT3OOc7CvhBTyPsTE/j0Ae6qvPLWOqcZwo15b8NtI/lRYMIpMXQfhZ69e9qzHma9O9lsoY5doDjJ1O530lDbcr8lAJ7DbBmwpNJW2yP9CKwbjQlDmhOLE2JA5oVS4tp+Vno1DDHP8yxQxX/ZOr3+zmNUWB+y/t5wIN9jiGiCfJZiKHU76TxdWChpGMlPRNYBmzocwwRTZDPQgylvjZP2d4n6e3Al6iGGa61va2LU054294nTYkDmhNLU+KAZsUCTOvPQqeGOf5hjh0mGb/spzSjRkREtJW5pyIiorYkjYiIqC1JIyIiakvSiIiI2jLLbcSQkfQrVFOOzKV6IPBBYIPt7QMNrKYS/1zgFts/aSlfYvv6wUXWGUlX2D570HHUIenXgO22H5H0bGAV8Aqq6Wv+yvaPJzzHsIyeauoHRdJ/pJpH6E7bX+7ztfM3eZqR9G7gLOBqqgcEoXowcBlwddNnypX0TuBcYDuwGDjP9rVl3222XzHA8CYkaeyzNAJeA/wLgO039j2oSZC0DXh5GfK9BvgZ8BngtFL+Xyc8xzAkjSZ9UCRttn1y2X4r1Qfgc8BvAv+nX7Hkb3LQeBqZTHtB0j3AibZ/Mab8mcA22wsHE1k9ku4Aft32TyQtoPof1idtf0TS7bZPGmyEByfpNqpv5ZdS/dsScBXV5w7bXxtcdBOTtN32S8v2k5K0pK22F094EtuN/wHuAZ7RpvyZwL19juX2lu2vA7PL9mHAHfmbDO5vUq75bmAr1W33m8rPqgNl/Yxlin6/bwEvalP+IuDuQcdXI/67xrw/HLgeuAjYOuj4asR/CPCnwEZgcSn7zqDjmkT8nwbeUrY/AYyU7eOAr9c5x7D0aTwOvAD47pjyY8q+fjpE0iyqfzyyvRfA9k8l7etjHPmbtLeC9t/ELwK2AY1uvqnhfGCTpHt5YpbcFwL/Hnj7oIKahO9LWmx7K4CrO443AGuB/zDQyGqw/TjwYUmfLq+7Ga6+4T8CPiLpvVQz894k6QGqf0t/VOcEw/LLnk9zPijPA26lui21pH9n+/uSDqf9zKVT5XzyN2mnScm052xfL+k4qj6juVR/31Gqb4n7BxpcPWcDT/oiYXsfcLakvxtMSJNnexQ4U9LrgUcGHU9drjq6/0DSc4EXU+WAUdu7655jKPo0ACQdQoM/KJKeA8yxvbOP18zf5KnXXAJ8DGibTD2Eo3MimmRokgaApDm0dG5OJjs+HUg60vZDA7ju820/3O/rjqfpyTRimA3Fw32SFku6Gfgq8AHgg8DXJN0sqa9D9CS9rFz3AUlrSlv+gX2b+xjHe1u2Tyijam6VdF8Zi91PP5D0z5JWSHp+n6/9FKXdeWf5+TawMwkjpiNJayXtkXRnjboflrS1/Nwj6eGOrjkMdxqStgJvs33LmPJTgL+z/fI+xnIj8H7gZqqOo7cAb7T97X4OGWwdLifpC8DHbF8n6WTgb2y/qh9xlOvfAVxANQR4CXAj1TDEa23/W7/iKLEsBv6Wqp9llOpOYx7wMPAntm/rZzwRU0nSbwA/Aa6wvWgSx70DOMn2H072msPSEX7Y2IQBYPtmSYf1OZbDW9rFPyTpVuB6SW+mzXKdffIC29cB2N5cnvTsp1/Y/jzw+XLt36Yat/5xSV+y/Xt9jOVyxv+C8Qmgb18wIqaa7RvK8y6/JOklwMeB2VQP773V9rfGHHoW8L5OrjksSeO68m36Cp7o3JxPNRKj3x2bkvS8MgoB21+R9N+AzwJH9jGOF5enUwXMk/Qc2z8r+57RxzigZYRUubNYD6yX9DzgjD7H0qQvGBGDsAb4Y9v3lqbqi4HXHtgp6UXAsZSn2CdrKJKG7XdK+i2eeMr3QOfmx21/sc/hfAB4KVXz1IH4vinpNOB/9DGOpWPeHwK/HCxwSR/jALiyXWFJrOv6HEuTvmBE9FUZ5v4q4NPSL7/LHTqm2jLgM5328w1Fn0bEZIzzBWPDAL5gREy50jz1eduLJB1BNTPAMQepfztwru3/18n1hmL01MFIWjnoGA5oSixNiQMGE4vt62z/se3ftv2Gsp2EEdOe7UeAnZLOhKotXdIv+/EkHQ/MAm7q9BpDnzTo/xPHB9OUWJoSBzQoliYl04hekHQVVQI4XtKopBXA7wMrJH2Dauqc1qbss6gmNO24iWlomqeaNHNpU2JpShxNi2U8kt5me2imqohooqG401A1DfjVVN9aN1PNpCrgKkmrno6xNCWOpsUygZ8POoCIYTcUdxpq0BoCTYmlKXE0LZaDkXS/7RcOOo6IYTYUQ25p1sylTYmlKXE0KhZJ3xxvFzCnn7FETEfDkjTOpznTgDcllqbE0bRY5gCnAz8aUy6goyGGEfGEoWiegmbNXNqUWJoSR5NikXQZ8AnbN7bZ96k+T2kSMe0MTdKIiIjBG4rRUxER0QxJGjFUJrN+QKn/u5LukrRN0qemOr6I6S7NUzFUJrN+gKSFVDPuvtb2jyQdbXtPP+KMmK5ypxFDxfYNwJOWtJX0EknXS7pV0r+Wp9MB3ko1E/KPyrFJGBFdStKI6WAN8A7brwT+jGr9AIDjgOMk/d+yRO+SgUUYMU0My3MaEW1NsH7ATGAh8GqqJV//VdIi2w/3OcyIaSNJI4bdIcDDthe32TcK3FymN9kp6W6qJPL1PsYXMa2keSqG2gTrB/wT8JpSfhRVc9V3BhFnxHSRpBFDZZLrB3wJ+KGku4CvAP/d9g8HEXfEdJEhtxERUVvuNCIiorYkjYiIqC1JIyIiakvSiIiI2pI0IiKitiSNiIioLUkjIiJq+/+Bn407oWPfiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "newbalanceOrig\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oldbalanceDest\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "newbalanceDest\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "isFraud\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in df.select_dtypes(exclude=['object']).columns:\n",
    "    print(column)\n",
    "    hist = df[column].hist(by=df[y_column], bins=30)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.793612</td>\n",
       "      <td>0.485213</td>\n",
       "      <td>-0.010605</td>\n",
       "      <td>0.147020</td>\n",
       "      <td>0.465154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <td>0.793612</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.906651</td>\n",
       "      <td>-0.018543</td>\n",
       "      <td>0.054395</td>\n",
       "      <td>0.324129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <td>0.485213</td>\n",
       "      <td>0.906651</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009689</td>\n",
       "      <td>-0.013352</td>\n",
       "      <td>0.143456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <td>-0.010605</td>\n",
       "      <td>-0.018543</td>\n",
       "      <td>-0.009689</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972684</td>\n",
       "      <td>-0.026609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newbalanceDest</th>\n",
       "      <td>0.147020</td>\n",
       "      <td>0.054395</td>\n",
       "      <td>-0.013352</td>\n",
       "      <td>0.972684</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isFraud</th>\n",
       "      <td>0.465154</td>\n",
       "      <td>0.324129</td>\n",
       "      <td>0.143456</td>\n",
       "      <td>-0.026609</td>\n",
       "      <td>0.047420</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "amount          1.000000       0.793612        0.485213       -0.010605   \n",
       "oldbalanceOrg   0.793612       1.000000        0.906651       -0.018543   \n",
       "newbalanceOrig  0.485213       0.906651        1.000000       -0.009689   \n",
       "oldbalanceDest -0.010605      -0.018543       -0.009689        1.000000   \n",
       "newbalanceDest  0.147020       0.054395       -0.013352        0.972684   \n",
       "isFraud         0.465154       0.324129        0.143456       -0.026609   \n",
       "\n",
       "                newbalanceDest   isFraud  \n",
       "amount                0.147020  0.465154  \n",
       "oldbalanceOrg         0.054395  0.324129  \n",
       "newbalanceOrig       -0.013352  0.143456  \n",
       "oldbalanceDest        0.972684 -0.026609  \n",
       "newbalanceDest        1.000000  0.047420  \n",
       "isFraud               0.047420  1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.corr())\n",
    "pd.plotting.scatter_matrix(df, figsize=(12, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature engineering\n",
    "\n",
    "- categorical feature: type 카테고리를 one-hot encoding\n",
    "- numeric feature: 정규화를 위한 min-max scaler 적용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>CASH_IN</th>\n",
       "      <th>CASH_OUT</th>\n",
       "      <th>DEBIT</th>\n",
       "      <th>PAYMENT</th>\n",
       "      <th>TRANSFER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CASH_IN  CASH_OUT  DEBIT  PAYMENT  TRANSFER\n",
       "0        0         1      0        0         0\n",
       "1        0         1      0        0         0\n",
       "2        0         1      0        0         0\n",
       "3        1         0      0        0         0\n",
       "4        0         1      0        0         0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot = pd.get_dummies(df['type'])\n",
    "one_hot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.40421180e-02, 2.79222709e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "        1.33426150e-03],\n",
       "       [2.16666530e-02, 8.79296806e-04, 0.00000000e+00, 9.60297766e-02,\n",
       "        9.82108144e-02],\n",
       "       [2.34636200e-02, 1.29565339e-03, 0.00000000e+00, 1.57574248e-03,\n",
       "        3.80722819e-03],\n",
       "       ...,\n",
       "       [6.31140928e-01, 1.10115521e-01, 0.00000000e+00, 6.49943163e-04,\n",
       "        6.06208582e-02],\n",
       "       [8.50002520e-02, 1.48300429e-02, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [8.50002520e-02, 1.48300429e-02, 0.00000000e+00, 6.17793265e-02,\n",
       "        6.99346086e-02]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_features = ['amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "transformed_data = scaler.fit_transform(df[numeric_features])\n",
    "transformed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.014042</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.021667</td>\n",
       "      <td>0.000879</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096030</td>\n",
       "      <td>0.098211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.023464</td>\n",
       "      <td>0.001296</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001576</td>\n",
       "      <td>0.003807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.005282</td>\n",
       "      <td>0.002054</td>\n",
       "      <td>0.003605</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.006387</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004332</td>\n",
       "      <td>0.004944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>0.033968</td>\n",
       "      <td>0.005926</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>0.631141</td>\n",
       "      <td>0.110116</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>0.631141</td>\n",
       "      <td>0.110116</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.060621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.014830</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.014830</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.061779</td>\n",
       "      <td>0.069935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4\n",
       "0     0.014042  0.000279  0.000000  0.000000  0.001334\n",
       "1     0.021667  0.000879  0.000000  0.096030  0.098211\n",
       "2     0.023464  0.001296  0.000000  0.001576  0.003807\n",
       "3     0.005282  0.002054  0.003605  0.000000  0.000000\n",
       "4     0.006387  0.000105  0.000000  0.004332  0.004944\n",
       "...        ...       ...       ...       ...       ...\n",
       "2995  0.033968  0.005926  0.000000  0.000000  0.003228\n",
       "2996  0.631141  0.110116  0.000000  0.000000  0.000000\n",
       "2997  0.631141  0.110116  0.000000  0.000650  0.060621\n",
       "2998  0.085000  0.014830  0.000000  0.000000  0.000000\n",
       "2999  0.085000  0.014830  0.000000  0.061779  0.069935\n",
       "\n",
       "[3000 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_df = pd.DataFrame(transformed_data)\n",
    "transformed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 19)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.concat([df, one_hot, transformed_df], axis = 1)\n",
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>CASH_IN</th>\n",
       "      <th>CASH_OUT</th>\n",
       "      <th>DEBIT</th>\n",
       "      <th>PAYMENT</th>\n",
       "      <th>TRANSFER</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>140421.18</td>\n",
       "      <td>C1667570766</td>\n",
       "      <td>16004.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C2102410298</td>\n",
       "      <td>0.00</td>\n",
       "      <td>140421.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014042</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>216666.53</td>\n",
       "      <td>C1495945377</td>\n",
       "      <td>50398.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C814408370</td>\n",
       "      <td>10119297.16</td>\n",
       "      <td>10335963.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.021667</td>\n",
       "      <td>0.000879</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096030</td>\n",
       "      <td>0.098211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>234636.20</td>\n",
       "      <td>C269129885</td>\n",
       "      <td>74262.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1389815469</td>\n",
       "      <td>166046.48</td>\n",
       "      <td>400682.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.023464</td>\n",
       "      <td>0.001296</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001576</td>\n",
       "      <td>0.003807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>52816.29</td>\n",
       "      <td>C129678616</td>\n",
       "      <td>117751.0</td>\n",
       "      <td>170567.29</td>\n",
       "      <td>C842027837</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005282</td>\n",
       "      <td>0.002054</td>\n",
       "      <td>0.003605</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>63871.25</td>\n",
       "      <td>C1282823885</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C1236511065</td>\n",
       "      <td>456488.36</td>\n",
       "      <td>520359.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006387</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004332</td>\n",
       "      <td>0.004944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type     amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
       "0  CASH_OUT  140421.18  C1667570766        16004.0            0.00   \n",
       "1  CASH_OUT  216666.53  C1495945377        50398.0            0.00   \n",
       "2  CASH_OUT  234636.20   C269129885        74262.0            0.00   \n",
       "3   CASH_IN   52816.29   C129678616       117751.0       170567.29   \n",
       "4  CASH_OUT   63871.25  C1282823885         6012.0            0.00   \n",
       "\n",
       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  CASH_IN  CASH_OUT  \\\n",
       "0  C2102410298            0.00       140421.18        0        0         1   \n",
       "1   C814408370     10119297.16     10335963.70        0        0         1   \n",
       "2  C1389815469       166046.48       400682.68        0        0         1   \n",
       "3   C842027837            0.00            0.00        0        1         0   \n",
       "4  C1236511065       456488.36       520359.60        0        0         1   \n",
       "\n",
       "   DEBIT  PAYMENT  TRANSFER         0         1         2         3         4  \n",
       "0      0        0         0  0.014042  0.000279  0.000000  0.000000  0.001334  \n",
       "1      0        0         0  0.021667  0.000879  0.000000  0.096030  0.098211  \n",
       "2      0        0         0  0.023464  0.001296  0.000000  0.001576  0.003807  \n",
       "3      0        0         0  0.005282  0.002054  0.003605  0.000000  0.000000  \n",
       "4      0        0         0  0.006387  0.000105  0.000000  0.004332  0.004944  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv('banking_fraud_final_dataset.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_tensorflow2_p36",
   "language": "python",
   "name": "conda_tensorflow2_p36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}