• Dr.S.Kanchana

The misclassification data leads to an extensive and persistent problem in many fields, which affect the accurate results that can be drawn from the analysis. The main action for dealing with missing values are to avoid, all incomplete data are implement with the rest of the information for designing process and change the incomplete values to perform the approximate calculation. This article proposes with the assistance of data mining approach, which can automatically predict whether the customer will subscribe a long term deposit. Research community proposed various suitable algorithms during the prediction of incomplete data analysis in terms of data mining approach. Various classifications of learning assignments are available in machine learning techniques including supervised and unsupervised techniques. This article describes the categories of various machine techniques in order to stand for the predictive analysis of misclassification data. The main advantage of these techniques is to generate more accurate data analysis without human expertise. This article organizes the process with few significant methods like Naïve Bayesian (NB), Decision Tree (DT), and Adaptive Boosting (ADAB) for the prediction of the misclassification data and also performs the comparison studies using predictive methods to find the best accuracy rate among them.

Adaptive Boosting, Decision Tree, Machine Learning, Naïve Bayesian, Supervised, Unsupervised

"IMPLEMENTATION OF MISCLASSIFICATION DATA USING PREDICTIVE CLASSIFICATION TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.335-340, June-2019, Available :http://www.jetir.org/papers/JETIR1906632.pdf

"IMPLEMENTATION OF MISCLASSIFICATION DATA USING PREDICTIVE CLASSIFICATION TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp335-340, June-2019, Available at : http://www.jetir.org/papers/JETIR1906632.pdf