• Ganji Pavan
• Dr.M.Jayanthi

The rapid shift towards online shopping during the COVID-19 pandemic gave rise to a significant increase in fake reviews on e-commerce platforms. These deceptive reviews often mislead consumers, distort market trust, and unfairly influence product ratings. This study explores the effectiveness of machine learning algorithms in detecting such fake reviews. Specifically, it investigates the performance of three key classifiers Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR), collectively referred to as the SKL approach. Through detailed analysis and evaluation, the research highlights the potential of these algorithms to detect and reduce the spread of fraudulent reviews in digital marketplaces.

Detecting Fake Reviews on E-Commerce Platforms Using Machine Learning During and After COVID-19

"Detecting Fake Reviews on E-Commerce Platforms Using Machine Learning During and After COVID-19", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.561-566, May-2025, Available :http://www.jetir.org/papers/JETIRGV06079.pdf

"Detecting Fake Reviews on E-Commerce Platforms Using Machine Learning During and After COVID-19", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. pp561-566, May-2025, Available at : http://www.jetir.org/papers/JETIRGV06079.pdf