Comparative study of different classification techniques using weka tool

In today's world data mining have increasingly become very large resulted in the great need of data mining technique in order to generate meaningful knowledge. Data mining is a fruitful technique to get useful information from a large amount of data stored in database. Data mining tools use to solve a big problem with data mining techniques such as classification, clustering, association rule, and neural network. Classification techniques are very effective way to classify the data, which is essential in decision-making process of the big problem. To solve the big problem in an effective manner by classification techniques has been challenges for researchers. The research describes classification algorithmic discussion of J48, Random forest, IBK, kStar, and Navy Bias. In this paper, compared the performance of successful classified instances, MAE RMSE, Kappa Statistics, and the error rate measurement for different classifier in weka using 10 fold cross-validation and results indicated none of the classification algorithm is perfect for all different database. It depends on the database