E-Commerce Fraud Detection Using Machine Learning
Prof. V.A Bijalpure*1, Shravani Jadhav*2, Neharika Deore*3, Joveriya Kazi*4, Ashra Shaikh*5
*1Prof, Department Of Computer Technology, K.K. Wagh Polytechnic, Nashik, Maharashtra, India.
*2,3,4,5Student, Department Of Computer Technology, K.K. Wagh Polytechnic, Nashik, Maharashtra, India.
ABSTRACT
In the rapidly growing landscape of online shopping, e-commerce platforms face a significant rise in fraudulent activities such as fake orders, stolen credit card usage, and account takeovers. These fraudulent transactions not only lead to substantial financial losses for companies but also damage customer trust and brand reputation. This project, "E-Commerce Fraud Detection Based on Machine Learning", aims to address these challenges by implementing a machine learning–based solution to accurately detect and prevent fraudulent transactions. The proposed system analyzes historical transaction data to identify behavioral patterns and anomalies that indicate potential fraud. Key transaction features—such as order amount, payment method, IP address, location, time of purchase, and device type are processed and evaluated using Python and libraries. Multiple algorithms, including Logistic Regression, Decision Tree, Random Forest, and XGBoost, are applied and compared to determine the most accurate and efficient model for fraud detection.The system classifies transactions as genuine or suspicious and can generate alerts for potentially fraudulent activities, enabling businesses to take immediate action. This solution is adaptable for e-commerce platforms like Amazon and Flipkart, payment gateways such as Paytm and Razorpay, and subscription services like Netflix. In the future, the system can be enhanced with real-time detection, dashboard integration, and deep learning techniques to further improve accuracy and efficiency, thereby safeguarding both businesses and customers.
Keywords E-commerce, Fraud detection, Machine learning, Online transactions, Anomaly detection, Transaction analysis, Cybersecurity, Python, Scikit-learn, Random Forest, Logistic Regression, Decision Tree, XGBoost, Customer behavior analysis, Fake orders, Credit card fraud, Account takeover, Data analytics, Predictive modeling
