In this paper, an attempt is made to identify and cross validate with five different classification methods in terms of precision, accuracy and kappa statistics calculated and visualized with different sets of database collected from different domain. This research paper has been implemented in R language environment and the obtained results show that which classifier is the most robust classifier method. The Accuracy based comparison of different classification for different datasets have been showed. By confusion matrix sensitivity, specificity, accuracy, true positive rate and false positive rate of different classifier for all four datasets are calculated and comparison of Kappa Statistics is also performed. The present work is about to analyze the effectiveness of the most popular classification techniques. According to the Experimental results, the Support Vector Machine model proved to have the best performance. It performed better of all datasets used. Naive Bayes Classifier, Decision Tree and Random Forest also performed well. The true positive rate and false positive rate table represent above 80% True Positive Rate and less than 20% False Positive Rate for all four datasets. Kappa Statistics basically performs the analysis between different classes. This shows the comparative analysis of different classification under the kappa statistics. Higher Value of kappa statistic is considered as good.

Decision Tree Random Forest Naive Bayes Classifier Linear Discriminant Analysis Support Vector Machine Confusion Matrix and Kappa Statistics.