Prediction of Credit Card Payment Next Month Through Tree Net Data Mining Techniques

Abstract

A number of research initiatives have recently been launched around the world regarding the conceptualization, specification, design and development principles of the future use of credit cards, storing secret information on them, while most time we use them for online payment. In addition, if it has enough money, we can pay for what we need at any time. Therefore, the goal of this proposed research is to use data mining techniques to predict credit card payment next month. Our proposed system contains five steps: (a) find the suitable database from the internet because this database is not available in Iraq, (b) pre-process the credit card database based on person correlation matrix to determine which feature is less correlated with other to remove it and reduce the time of prediction, (c) split pre-processing database into two parts training and testing dataset, (d) apply TreeNet prediction data mining techniques (TPDMT) on training dataset to test if we need payment next month or do not, find the optimal tree. TreeNet based on Boosting Machine usually makes the predictor to use Decision Trees (DTs). (e ) Finally, pass the testing dataset on the optimal tree results from TPDMT, then using the five measures related to confusion matrix to evaluate the results including “Accuracy (AC), recall or true positive rate (TP), precision (P), F-measure (considers both precision and recall) and Fb”.

Implementation of a Breakpoint Halfway Discretization to Predict Jakarta's Air Quality

“…The multinomial naive Bayes (MNB) and decision tree-ID3 (DT) methods (Pan et al, 2018;Chen & Fu, 2018;James et al, 2013;Han et al, 2012) were employed in this study to develop a predictive model of Jakarta's air quality. These two methods are frequently successful in achieving accuracy while doing prediction/classification tasks (Kresnawat et al, 2021;Hussein et al, 2020;Chou & Lo, 2018). In addition, we proposed all midway breakpoints and halfway mixture breakpoints for the discretization of the numerical variables (Witten & Frank, 2005).…”