AI-Powered GST Analytics: Detecting Fraud Using Machine Learning Approach

This paper involves creating AI and machine learning (ML) solutions using a large dataset provided by the Goods and Services Tax Network (GSTN). The dataset contains about 900,000 records with 21 attributes. The goal of this project is to use data to build models that can predict specific outcomes and uncover patterns that help improve tax systems and detect fraudulent activities. To achieve this, a predictive model will be created and trained on a portion of the available dataset. This training helps the model learn from existing data and reduce errors in its predictions. After training, the model will be tested on new, unseen data to check how accurately it can make predictions in real-world scenarios.