An Intelligent System For Identifying Fake Reviews In E-commerce Websites

Abstract

The Rapid Growth Of E-commerce Platforms Has Increased The Influence Of Online Customer Reviews On Purchasing Decisions. However, The Presence Of Fake Or Deceptive Reviews Has Become A Major Challenge, Misleading Customers And Affecting The Credibility Of Online Marketplaces. Detecting Such Fraudulent Reviews Is Essential To Ensure Trust And Transparency In Digital Commerce. This Project Proposes A Machine Learning-based Fake Review Detection System Integrated Into A Web-based E-commerce Environment. The System Allows Users To Browse Products And Submit Reviews, Which Are Automatically Analyzed Using Natural Language Processing (NLP) Techniques And Classified As Genuine Or Fake Using Decision Tree And XGBoost Algorithms. The Reviews Are Preprocessed And Converted Into Numerical Features Using The TF-IDF Technique. The Models Are Evaluated Using Performance Metrics Such As Accuracy, Precision, Recall, And F1-Score. The Classified Results Are Stored In A Database, And Only Genuine Reviews Are Considered For Product Ratings. An Admin Dashboardprovides Insights Into Fake Review Statistics And Model Performance. By Integrating Machine Learning With A Real-time Web Application, The Proposed System Improves The Reliability Of Online Reviews And Enhances User Trust In E-commerce Platforms.

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