Unveiling Big Data Insights: A Neutrosophic Classification Approach for Enhanced Prediction with Machine Learning

The ever-growing volume and complexity of Big Data pose challenges for traditional classification tasks. This paper explores the potential of Neutrosophic Sets (NS), a powerful framework for handling uncertainty, in building robust classification models for Big Data prediction using Machine Learning (ML) techniques. We provide a detailed background on NS and discuss its advantages over Fuzzy Sets. We then propose a methodology that integrates NS with relevant ML algorithms for classification. We evaluate the performance of our Neutrosophic-based model on a Big Data source. The results are analyzed to assess the effectiveness of the Neutrosophic approach for Big Data prediction. This research contributes to the advancement of uncertainty management in Big Data classification and paves the way for further exploration of Neutrosophic-based ML models for various prediction tasks. Results show that the Neutrosophic Neural Networks (NNs) model achieved commendable performance across various metrics, with an accuracy of 79.08%, precision of 74.58%, recall of 77.64%, and an F1-score of 75.63%. These metrics indicate that the Neutrosophic NNs model effectively balances the trade-offs between precision and recall, providing a robust classification performance in the context of the evaluated datase