Data Mining Techniques in Decision Making

Abstract

Data mining aims to find relevant information for decision-making, forecasting, optimising, and other business or research reasons. In this study, data mining is used in the domain of decision-making. Various problems for this domain exist in the literature, including selection problems, considering unnecessary attributes, and removing irrelevant attributes in the dataset by applying different preprocessing techniques. For this purpose, a smartphone dataset is used, and different machine learning classifiers are applied. Decision Tree, Naive Bayes, SMO, bagging, and Random Forest were chosen for precision, recall, and F-measure. Results demonstrate that Random Forest obtains 57% accuracy and performs better on average than the other algorithms. The class "Saving Account" was classified with 52% accuracy as of the other attributes, but it has the fewest errors depending on the attributes. This study can be extended by applying the proposed methodology to a rich volume dataset and deep-learning techniques.

Keywords

• Data mining, Classification, Decision Trees, Random Forest, Decision Making, Marketing