A Machine Learning Approach for E-Commerce Counterfeit Product Detection Using Transactional and Behavioral Data

Abstract: Identifying fraudulent transactions has become a crucial task for maintaining digital security and customer confidence due to the quick growth of e-commerce platforms. Based on customer, payment, and behavioral characteristics, this study introduces a Counterfeit Transaction Detection System that uses the Random Forest algorithm to identify transactions as either authentic or fraudulent. To increase the accuracy and dependability of the model, the dataset was preprocessed using techniques like feature engineering, encoding, scaling, and data cleaning. The suggested model performed well on precision, recall, and F1-score metrics, achieving a high classification accuracy of 96.85%. Cross-validation methods were used to improve generalization and reduce overfitting. A Streamlit-based interface was used to deploy the trained model, allowing users to upload transaction data and get predictions about authenticity in real time. All things considered, this study demonstrates how well machine learning works to prevent online fraud and improve transaction security in e-commerce platforms.

Keywords: Counterfeit Detection, E-Commerce, Machine Learning, Random Forest, Fraud Analytics, Feature Engineering