A Study on Markov Models and its Applications in Machine Learning

The work focuses on the study of Markov models and their applications in machine learning, particularly in data classification. Various machine learning classification techniques are reviewed and implemented using real-world datasets such as the Credit Card Dataset and Air Quality Index Dataset. Their performance is analyzed to understand their effectiveness in solving classification problems. The study investigates the application of Markov Decision Processes (MDPs) for classification tasks. MDPs provide a framework for decision-making under uncertainty and are applied to classify the selected datasets. The effectiveness of Hidden Markov Models (HMMs) in capturing probabilistic relationships and hidden patterns within data is also examined. HMM algorithms are implemented and used for data classification. In addition, the Expectation Maximization (EM) algorithm is studied and applied to classification problems. A comparative analysis of the various methods is carried out, highlighting their merits, demerits, and limitations. The concepts of misclassification and error bounds are also discussed to evaluate the accuracy and reliability of the classification models.