Advanced AI-Driven Data Science Framework for Optimized Predictive Analytics and Classification

Abstract

In the era of data expansion across domains, there is a need for robust structures of predictive analytics. In this paper, we introduce an advanced data science framework that combines ensemble machine learning (Random Forest, XG Boost, Light GBM and deep learning) with a novel query fragment caching mechanism. The framework automates feature engineering and adaptive model selection, inference acceleration. The experimental results on several benchmark datasets validate the superior classification accuracy of the ensemble, and the best result is obtained by Light GBM (92.4%) and XG Boost (90.0%). The caching mechanism can increase cache hit rates up to 19.2% and reduce query latency up to 28.7% which can greatly enhance throughput. The comparative analysis further shows superior performance of the proposed approach in terms of precision, recall, and F1 score when compared with existing state-of-the-art methods, making it a promising and effective solution for large-scale predictive analytics and classification tasks.