Analysis of Credit Card Fraud Detection Performance Using Random Forest Classifier & Neural Networks Model
Journal Title: Engineering and Technology Journal - Year 2024, Vol 9, Issue 02
Abstract
This research discusses credit card fraud detection using machine learning algorithms, specifically Random Forest Classifier and Neural Networks. Research methods include the EDA (Exploratory Data Analysis) stage, data preprocessing, building Random Forest and Neural Networks models, as well as model evaluation. The data used comes from the Kaggle dataset that has been provided. Data analysis is carried out using Pandas to understand the structure and content of the data, while preprocessing involves checking for duplicates, displaying data information, and statistical descriptions. The research results show that the Random Forest model achieved an accuracy of 96% in detecting credit card fraud, while Neural Networks also provided good results. A comparison of the performance of the two algorithms shows that both are effective in detecting fraud. Suggestions for further development include comparing the performance of the model with other algorithms, exploring the factors that influence fraud detection, and developing a more complex and adaptive detection system. The positive implication of the results of this research is increased efficiency in credit card fraud detection, which can provide major benefits in protecting consumers and financial institutions from detrimental fraudulent activities. References used in the research are also included to support the validity and accuracy of the findings obtained
Authors and Affiliations
Steven Wijaya,Wilfredo Wesly, Kristina Ginting, Abdi Dharma,
Keywords
Related Articles
- EP ID EP731072
- DOI 10.47191/etj/v9i02.11
- Views 120
- Downloads 0
ASSESSMENT OF TRACE AND RARE EARTH ELEMENT DISTRIBUTION AND SPECIATION IN BAUXITE DEPOSITS ACROSS EKITI AND ONDO STATES, NIGERIA
The growing significance of rare earth elements (REEs) in advanced electronic technologies and the nuclear industry, coupled with concerns about geopolitical dynamics and supply market vulnerabilities, has reignited glob...
Covid-19 Review: A Trend Analysis of the Impact of Religious Festivities and Non-Adherence to Social Distancing in Nigeria
The global negative impact of Covid-19 pandemic necessitates curtailing measures and policies in case of future outbreak. A trend analysis of the spread of Covid-19 showing the effect of social gatherings especially duri...
Analysis of Noise Level in Soekarno Hatta International Airport Area
One of the environmental impacts in the operation of an airport is noise, which originates from aircraft activities. Soekarno-Hatta International Airport is a public service airport located in Tangerang. This study aims...
The Challenges of Implementing Building Information Modeling in Indonesia Construction Projects
The future of BIM is exciting and full of challenges, but there are several obstacles to implementing BIM effectively in the construction sector in Indonesia. Thus, the aim of this research is to examine the challenges o...
Deep and Machine Learning for Improving Breast Cancer Detection
Breast cancer is the most common type of cancer in the world, as the number of people infected with it reached 2.2 million women in 2020, and World Health Organization reports indicated that the incidence of it is 1 to 1...