Analysis of Online Transaction using Data Analytics Framework

Nowadays, online transactions become a necessity for everyone; thus, they generate a vast amount of data, which requires a robust framework to ensure their security, efficiency, and reliability. This research paper explores the application of advanced data analytics techniques to ensure and enhance the confidentiality of the online transaction process. Using this analytics framework, we can analyse patterns, detect anomalies, and predict trends with online transaction data. An online survey was conducted to collect data from one lakh consumers of different geographical regions and diverse working groups. Descriptive analysis has been used in this study to ascertain the present state of online transactions. The study investigates the significance of feature selection, anomaly detection, and clustering methods in identifying patterns, trends, and potential fraud indicators within online transactions. The findings of this research contribute to the growing body of knowledge on leveraging data analytics frameworks to extract valuable insights from online transaction data.