World Wide Web is a huge warehouse of web pages and links. It offers large quantity of data for the Internet users. The growth of web is incredible as around one million pages are added per day. Users’ accesses are recorded in weblogs. Web usage mining is a variety of mining technique in logs. Because of the outstanding usage, the log files are growing at a faster rate and the size is suitable very large. This leads to the complexity for mining the practice log according to the needs. This provides a vast field for the researchers to supply their proposal to develop a better mining technique. In this paper, we analyse and study Markov model and allKth Markov model in Web prediction. We propose a new customized Markov model to ease the issue of scalability in the number of paths. In adding, we there a new two-tier prediction structure that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can advance the prediction time without compromising prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and relationship rule mining. Our experiments demonstrate the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our investigation conclusions that accuracy improves with higher orders of all-Kth model

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