• Jismy Joseph
• Dr.G. Kesavaraj

Now a day’s, Ensemble methods are some of the most influential strategies in data mining and machine learning. It combines multiple learning algorithms into one, to obtain a more accurate predictive result. Credit risk analysis is one of the serious tasks in financial sector. By using ensemble methods the credit data can be classified more perfectly than by using a basic model. This paper represents a comparative study of different classifier on credit data set when the ensemble learning method ‘Bagging’ is used. This study observed that Bagging method can improve the accuracy of the basic classifier.

Ensemble learning, Classification, Bagging, Machine Learning

"Ensemble Learning Technique to Improve Classification Accuracy for Credit Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.352-356, May-2019, Available :http://www.jetir.org/papers/JETIR1905D49.pdf

"Ensemble Learning Technique to Improve Classification Accuracy for Credit Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp352-356, May-2019, Available at : http://www.jetir.org/papers/JETIR1905D49.pdf