The technique of drawing learning out of a massive amount of data is called data mining. Sequencing analysis, rules of association, and categorization are the main elements of data mining approaches. Considering its vast applications, classification is a crucial data analysis technique for classifying the many types of data utilized in almost every aspect of our lives. One method of data mining (machine learning) that is employed to determine the group affiliation for data occasions is categorization. The fundamental categorization methods, including random forest, J48, Naïve Bayes, and Naïve Bayes Simple, are tested in this work. The purpose of this work is to present an interdisciplinary overview of several data mining methods for categorization and to test the reliability of divergent classifications using a range of datasets, including iris, diabetes, and soybean. Additionally, the study addresses the most effective first search technique's application in choosing of qualities depending on their relevance.