Project Abstract

Abstract
The rise of online transactions has brought about a corresponding increase in fraudulent activities, posing significant challenges to the security of e-commerce platforms. Machine learning algorithms have shown promise in detecting and preventing fraud in real-time. This research project aims to enhance the effectiveness of machine learning algorithms for fraud detection in online transactions. The study will focus on developing and improving existing algorithms to accurately identify fraudulent activities while minimizing false positives. The research will begin with an introduction providing an overview of the problem statement, objectives, limitations, scope, significance, and the structure of the study. A comprehensive literature review will be presented to explore existing research on fraud detection, machine learning algorithms, and their applications in the field of online transactions. This chapter will also highlight the gaps in current literature and provide a theoretical framework for the research. The methodology chapter will outline the research design, data collection methods, and the machine learning techniques to be employed in the study. The research will utilize a dataset of online transaction records to train and test the algorithms. Various performance metrics will be used to evaluate the effectiveness of the algorithms in detecting fraud accurately. The findings chapter will present a detailed analysis of the results obtained from the experiments conducted. The discussion will focus on the strengths and limitations of the algorithms, as well as potential areas for further improvement. The chapter will also highlight the implications of the findings for the field of online transaction security. In the conclusion and summary chapter, the research findings will be summarized, and the overall contributions of the study to the field of fraud detection in online transactions will be discussed. Recommendations for future research directions will be provided to further enhance the effectiveness of machine learning algorithms in combating fraud in e-commerce platforms. Overall, this research project seeks to advance the state-of-the-art in fraud detection by improving machine learning algorithms for online transactions. The findings of this study are expected to have significant implications for enhancing the security and trustworthiness of e-commerce platforms, ultimately benefiting both businesses and consumers alike.

Project Overview