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Introduction

With the rise of e-commerce platforms, there has been a corresponding increase in the number of fake product listings being posted by sellers. These fake listings not only deceive consumers but also harm the reputation of the e-commerce platforms themselves. Therefore, there is a pressing need for automated tools to detect and remove these fake listings in a timely manner. This thesis explores the use of machine learning algorithms and data analytics techniques to develop a system for automated detection of fake product listings on e-commerce platforms.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of e-commerce platforms
2.2 Fake product listings in e-commerce
2.3 Existing methods for detecting fake listings
2.4 Machine learning algorithms for fake listing detection
2.5 Data analytics techniques for fake listing detection
2.6 Case studies on fake listing detection
2.7 Challenges in detecting fake listings
2.8 Ethical considerations in fake listing detection
2.9 Future trends in fake listing detection
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Feature selection
3.5 Algorithm selection
3.6 Model training
3.7 Model evaluation
3.8 Performance metrics
3.9 Ethical considerations
3.10 Summary of research methodology

Chapter 4: Discussion of Findings
4.1 Performance of machine learning algorithms
4.2 Effectiveness of data analytics techniques
4.3 Comparison with existing methods
4.4 Case study results
4.5 Limitations of the study
4.6 Future research directions
4.7 Implications for practice
4.8 Recommendations for e-commerce platforms
4.9 Conclusion of findings

Chapter 5: Conclusion and Summary
5.1 Summary of research findings
5.2 Contribution to knowledge
5.3 Practical implications
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview

The Automated detection of fake product listings on e-commerce platforms has become a critical issue due to the proliferation of fake listings that deceive consumers and damage the reputation of e-commerce platforms. This thesis aims to address this challenge by developing a system that uses machine learning algorithms and data analytics techniques to detect and remove fake product listings automatically. The research methodology involves collecting and preprocessing data, selecting features, choosing algorithms, training models, and evaluating performance. The findings of the study will be discussed in detail, including the performance of the algorithms, the effectiveness of the techniques, and comparisons with existing methods. The thesis will conclude with recommendations for e-commerce platforms, implications for practice, and suggestions for future research in this area.

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