-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathresearchdatamining.html
143 lines (135 loc) · 7.39 KB
/
researchdatamining.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
<!DOCTYPE HTML>
<html>
<head>
<title>Research Data Mining</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
<link rel="stylesheet" href="assets/css/main.css" />
<noscript><link rel="stylesheet" href="assets/css/noscript.css" /></noscript>
</head>
<body class="is-preload">
<div id="page-wrapper">
<!-- Header -->
<header id="header">
<nav id="nav">
<ul>
<li><a href="https://ncthetruth.github.io/">Home</a></li>
<li>
<a href="#">Project</a>
<ul>
<li>
<a href="#">Binus Chat</a>
<ul>
<li><a href="https://ncthetruth.github.io/binuschat">Binus Chat - Web</a></li>
<li><a href="https://ncthetruth.github.io/binuschat">Soon (Binus Chat - Mobile)</a></li>
</ul>
<li><a href="#">Research Data Mining</a></li>
<li><a href="https://ncthetruth.github.io/earlycare">Early Care</a></li>
<li><a href="https://ncthetruth.github.io/virtualnursingassistant">Virtual Nursing Assistant</a></li>
<li><a href="https://ncthetruth.github.io/tanakaguitar">Tanaka Guitar</a></li>
<li><a href="https://ncthetruth.github.io/givemesomecredit">GiveMeSomeCredit</a></li>
</ul>
</li>
<li><a href="https://ncthetruth.github.io/#one">About</a></li>
<li><a href="#">Experience</a>
<ul>
<li><a href="https://ncthetruth.github.io/himti">HIMTI</a></li>
<li><a href="https://ncthetruth.github.io/ureekabinus">Ureeka Binus</a></li>
<li><a href="https://ncthetruth.github.io/graphicdesign">Graphic Design</a></li>
<li><a href="https://ncthetruth.github.io/otherexperience">Other Experience</a></li>
</ul>
</li>
</ul>
</nav>
</header>
<!-- Main -->
<div id="main" class="wrapper style1">
<div class="container">
<header class="major">
<h2 class="bld">Comparison of Data Mining Algorithm for
Credit Card Fraud Detection</h2>
<p>Comparison of Algorithms with Ensemble Voting</p>
</header>
<!-- Content -->
<section id="content">
<a href="#" class="image fit"><img src="images/research.webp" alt="researchdatamining" /></a>
<div class="pmid">
<h3>Why are we conducting this research?</h3>
<p>During pandemic, credit card usage has increased
rapidly compared to the year before. Credit cards fraud has been
a massive threat for their users. Through the survey and research,
some algorithms have been found to decrease the number of fraud
Leveraging machine learning and data mining techniques for
credit card fraud detection. This research journal aims to
investigate and propose the most efficient approach for developing
an algorithm that effectively detects credit card fraud.</p>
<h3>Why do we conduct research on this topic?</h3>
<p>In modern society, particularly in the banking sector, credit cards
have become a widely used payment tool. They offer convenience for
transactions but require users to pay their outstanding balances monthly.
However, the convenience of credit cards is countered by the risk of credit
card fraud, including carding, where attackers steal user information to
make unauthorized transactions. In 2021, there were 389,737 reported cases
of credit card fraud globally, resulting in losses of $32.34 billion. To
address this issue, this research explores the effectiveness of various
methods, including Random Forest (RF), Logistic Regression (LR), Support
Vector Machines (SVM), and Ensemble Methods, to classify and detect
potentially fraudulent transactions, ultimately contributing to improved
credit card fraud detection models.</p>
<h3>What do we do?</h3>
<p>We combined two types of algorithms, SVM (Support Vector Machine) and Logistic
Regression, SVM and Random Forest, and Random Forest and Logistic Regression.
We used data from Kaggle, which contained one million rows, and split it into
75% for training data and 25% for testing data. We also utilized the scikit-learn library.
To combine these two algorithms, we employed Ensemble Learning, a method where we leveraged
two different algorithms and conducted a voting process on their results. This concept is
similar to Random Forest, but with the distinction that we used different algorithms,
making it a heterogeneous ensemble approach.</p>
<h3>What were the results?</h3>
<p>Based on the experiments conducted, SVM and Random Forest proved to be the most effective
combination of algorithms. This combination exhibited the highest accuracy performance,
with precision at 100%, recall at 99%, F-Measure at 99%, and an overall accuracy rate of 100%.
These results highlight that employing a voting mechanism to create a hybrid model can
significantly enhance the accuracy and performance of base classifier models. This approach
also opens up possibilities for using different combinations of base classifier methods to
create more efficient and faster credit card fraud detection models.
Our work demonstrates that combining algorithms, such as SVM and Random Forest, can achieve
the highest accuracy. In future studies, we plan to explore the utilization of real-time credit
card transaction data for fraud detection. This research aims to enhance the effectiveness of
fraud detection algorithms by leveraging ensemble classifier methods and developing more refined
algorithms tailored to credit card fraud cases. The ultimate goal is to improve the overall
efficiency of credit card fraud detection systems.</p>
</div>
<h3>Contribution</h3>
<ul>
<li>Nicholas Christopher</li>
<li>Feivel Gunawan</li>
<li>Justin Jefferson</li>
</ul>
</section>
</div>
</div>
<!-- Footer -->
<footer id="footer">
<ul class="icons">
<li><a href="https://www.linkedin.com/in/nicholas-christopher-081237146/" class="icon brands fa-linkedin-in"><span class="label">LinkedIn</span></a></li>
<li><a href="https://www.instagram.com/ncthetruth" class="icon brands fa-instagram"><span class="label">Instagram</span></a></li>
<li><a href="https://github.com/ncthetruth" class="icon brands fa-github"><span class="label">GitHub</span></a></li>
</ul>
<ul class="copyright">
<!-- <li>© Untitled. All rights reserved.</li><li>Design: <a href="http://html5up.net">HTML5 UP</a></li> -->
<li>ncthetruth.github.io</li>
</ul>
</footer>
</div>
<!-- Scripts -->
<script src="assets/js/jquery.min.js"></script>
<script src="assets/js/jquery.scrolly.min.js"></script>
<script src="assets/js/jquery.dropotron.min.js"></script>
<script src="assets/js/jquery.scrollex.min.js"></script>
<script src="assets/js/browser.min.js"></script>
<script src="assets/js/breakpoints.min.js"></script>
<script src="assets/js/util.js"></script>
<script src="assets/js/main.js"></script>
</body>
</html>