Data mining concept is growing fast in popularity, it is a technology that involving methods at the intersection of (Artificial intelligent, Machine learning, Statistics and database system), the main goal of data mining process is to extract information from a large data into form which could be understandable for further use. Some algorithms of data mining are used to give solutions to classification problems in database. In this paper a comparison among three classification’s algorithms will be studied, these are (K- Nearest Neighbor classifier, Decision tree and Bayesian network) algorithms. The paper will demonstrate the strength and accuracy of each algorithm for classification in term of performance efficiency and time complexity required. For model validation purpose, twenty-four-month data analysis is conducted on a mock-up basis.
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