Fake Review Detection Using Machine Learning and Deep Learning, Distilled-Bert

In the digital era, online reviews significantly influence consumer decisions and business credibility. However, the increasing manipulation of reviews through deceptive or fake content poses a serious challenge to both customers and service providers. This project presents a machine learning-based approach to identify and filter out fake reviews by analyzing textual patterns and user behaviors. Using supervised algorithms such as Support Vector Machines (SVM) and Decision Trees (DT), along with natural language processing techniques, the system classifies reviews as genuine or fraudulent. The architecture supports user interaction via a GUI built in Python, integrated with a secure login system. The effectiveness of the proposed model is validated through multiple testing strategies, ensuring reliability and performance. By automating the detection of fake reviews, this system aims to restore trust in online platforms and aid users in making informed purchasing decisions.