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The Web has become the world's largest knowledge repository. Web usage mining focuses on discovering the potential knowledge from the browsing patterns of users and to find the correlation between the pages. With exponential growth of web log, the conventional data mining techniques were proved to be inefficient. As web log is incremental in nature, it becomes a crucial issue to predict exactly the ways how users browse websites. It is necessary for web miners to use predictive mining techniques to extract the usage patterns and study the visiting characteristics of user. The data on the web log is heterogeneous and non scalable, hence to reduce the operation scope and increase the accuracy precision significantly an improved hybrid model is required. This paper introduces an efficient hybrid predictive model, which is a combination of Markov model and Bayesian theorem. This two stage predictive model to enables the web miner to identify and analyze web user navigation patterns. In this model, the Markov model helps to reduce the operations scope by filtering possible categories and Bayesian theorem improves accuracy in predicting the web pages in identified category. To validate the proposed prediction model, several experiments were conducted and results proven this are claimed in this paper.