Machine Learning (ML) has become a cornerstone of modern data-driven decision-making systems. However, challenges such as data heterogeneity, model interpretability, and scalability continue to limit its real-world applicability. This thesis proposes a hybrid deep learning framework that integrates traditional statistical methods with advanced neural architectures to improve predictive accuracy while maintaining interpretability.
The study focuses on combining feature engineering techniques with deep neural networks and explainable AI (XAI) methods to create a robust, scalable, and transparent system. Experimental results demonstrate improved performance across multiple datasets, with enhanced model explainability and reduced computational overhead.
The rapid growth of data across industries has necessitated the development of intelligent systems capable of extracting meaningful insights. Machine Learning, particularly deep learning, has shown exceptional performance in domains such as healthcare, finance, and education.
Despite these advancements, several challenges persist:
Lack of interpretability in deep learning models
Data inconsistency and noise in real-world datasets
High computational cost
Difficulty in deploying scalable ML systems
This research aims to address these challenges by designing a hybrid framework that balances performance, interpretability, and efficiency.
