Enhancing Online Security: A Random Forest Classifier Approach to Payment Fraud Detection

<p>The rise of the internet and e-commerce appears to entail the usage of online payment transactions. The increased usage of online payments is leading to a rise in fraud. However, as the number of online transactions increases, so does the number of fraud instances. Fraud detection is an important component of online payment systems since it serves to protect both customers and merchants from financial damages. In this project, we propose a fraud detection system for online payments that uses machine learning techniques to identify and prevent fraudulent transactions. Using machine learning algorithms, we can find unique data patterns or uncommon data patterns that will be useful in detecting any fraudulent transactions. The Random Forest Classifier will be utilized to get the best results. Our approach strives to improve fraud detection accuracy while reducing the amount of false positives, resulting in a more efficient and effective method for identifying and combating fraud.</p><p>Keywords: Fraud Detection, Machine Learning, Random  Forest Algorithm, SVM, Classification, Data Pre- Processing, Prediction.</p&gt