WEKA Approach for Comparative Study of Classification Algorithm

This paper discusses data mining techniques to process a dataset and identify the relevance of classification test data. Mining tools to solve large amounts of problems such as classification, clustering, association rule, neural networks, it is a open access tools directly communicates with each tool or called from java code to implement using this. In this paper we present machine learning data mining tool used for different analysis, Waikato Environment for Knowledge Analysis is introduced by university of New Zealand it has capacity to convert CSV file to Flat file. Our work shows the process of WEKA analysis of file converts and selection of attributes to be mined and comparison with Knowledge Extraction of Evolutionary Learning not only analysis the data mining classifications but also the genetic, evolutionary algorithms is the best efficient tool in learning. We have provided an evaluation based on applying these classification methods to our dataset and measuring the accuracy of test results.