A PySpark-based KNN classification framework for detecting fake product reviews in e-commerce

• Advanced data analytics and PySpark improve fake product review detection. • Classification and clustering methods identify patterns and reduce deceptive reviews. • Visual data exploration aids anomaly detection in review characteristics. • Techniques protect consumers and foster trust in e-commerce platforms. • Study bridges theoretical research and practical spam detection applications. In e-commerce, online reviews significantly influence consumer purchasing behavior, with authenticity directly impacting business revenues and consumer trust. This study addresses the critical issue of fake product reviews (FPRs) by analyzing their effects on consumer choice and the overall reliability of online marketplaces. To improve FPR detection, we employed PySpark and advanced data science techniques to analyze a labeled dataset of user reviews, uncovering patterns and anomalies indicative of review manipulation. By integrating classification methods such as K-Nearest Neighbors (KNN), the study demonstrates how machine learning can effectively identify and mitigate the impact of FPRs, thereby enhancing the credibility of online reviews. The results contribute to ensuring fair competition, consumer protection, and the long-term integrity of digital commerce. Future research may expand this framework by incorporating additional datasets, alternative classification algorithms, and deeper linguistic or sentiment-based analyses.