A Comparative Study and enhancement of classification techniques using Principal Component Analysis for credit card dataset

The following research reveals the significance of modified classification in estimating new trends. Rigorous evaluation of different classification algorithms viz. Logistic Regression, Decision Tree, K-Nearest Neighbor and Naive Bayesian is explored in this paper. These findings forecast the finest techniques for discovery of potential defaulters which can be adapted by banking institutions. Our main motive is to compare the performance measures between original dataset and original dataset on which principal component is applied. The reason to use the principal component was to evaluate its impact on the performance of the algorithms used while dealing with the dataset. Different algorithms can be compared on the basis of various criterions such as Accuracy, Precision, Fl-Score, Recall, ROC. Successful contrast between these attributes would yields a efficient model for the given dataset. Logistic regression is then found to be the most efficient method for this particular dataset.