Comparative Study on Efficiency of Classifier Models

This work deals with the implementation of various classifier models, each run on a training set, cross-validation technique and a percentage split with best accuracy value and the accuracy values obtained by each of the classifier models under the "cross-validation" technique has been recorded. Data classification refers to the process in which data can be separated into distinct categories or levels so that essential data can be made easier to find and be retrieved. The proposed work involves analysis of the techniques of each of the classifier models and their accuracy values are compared so as to determine the best algorithm while working with all the records and attributes of the publicly available data set "Statlog Heart" of the UCI repository.