Survey Paper on Machine Learning and Deep Learning Driven Applications using Bayesian Techniques

In today's data-driven world, Bayesian methods, including Naive Bayes and Bayesian Networks, have become essential tools for solving complex data analysis and classification challenges. These techniques, grounded in Bayesian probability theory, excel in categorizing data based on observed features, making them valuable in various fields like medicine and image classification. Naive Bayes, though seemingly simple, proves effective in applications like spam filtering and sentiment analysis by calculating probabilities using Bayes' theorem. Bayesian Networks, more complex graphical models, capture intricate relationships among variables, finding uses in healthcare and engineering. These methods have been showcased in research papers tackling real-world issues, from image classification to medical diagnostics, highlighting their potential to shape the future of data analysis and machine learning in our data-rich era.