Advanced Big Data Learning and Predictive Analytics: Techniques, Applications, and Future Directions

The increasing rate of data in all industries has made the advanced Big Data learning and predictive analytics more significant to make the data driven decisions. This survey presents an in-depth review of the state of the art learning methods, such as machine learning, deep learning, ensemble, and hybrid models, with a focus on their capacity to draw actionable knowledge out of large and heterogeneous data. Some of the most important predictive analytics techniques including statistical modeling, machine learning(ML), and deep learning(DL) techniques are explored in terms of their use in healthcare, finance, manufacturing, smart cities, and marketing. Potential issues such as data quality, computational complexity, privacy, security, and model interpretability have been identified, as well as the latest trends, such as explainable AI, AI-augmented analytics, multi-modal learning, federated and edge computing, and sustainable analytics practices. Lastly, the future research directions are addressing real-time predictive analytics, knowledge graphs and semantic AI, privacy-preserving methods, and adaptive self-learning systems.