Detection-of-Business-Anomalies-using-Autoencoders

The objective of this project is to develop and utilize autoencoders for detecting anomalies in credit card transactions. The objective of this project is to develop and utilize autoencoders for detecting anomalies in various business datasets. The project employs a sophisticated blend of technologies and tools to detect anomalies in financial datasets, leveraging the capabilities of machine learning and statistical analysis. The core of aptheproach is implementation of autoencoders, specifically Variable Autoencoders (VAEs), combined with the XGBoost algorithm. This ensemble method has proven effective in handling the challenges presented by the complex nature of financial data. Dataset: The analysis encompasses a diverse range of dataset, from Kaggle