International Conference on Data Science, Machine Learning and Statistics, Van, Türkiye, 26 - 29 Haziran 2019, ss.280-282
The developments in Data Science have also enabled the emergence of data which can support the decision
making processes of the companies. Data Mining (DM), which includes the processes of data acquisition, storage
and analysis, has been successfully implemented in different business problems. In this study, the size of the firms
that the shares belong to will be estimated by classification analysis by means of variables such as the type of the
transaction, quantity and amount of the shares. This analysis is thought to be useful in exploring investor behaviors.
In this context, three different classification techniques, Decision Trees (DT), Logistic Regression (LR) and Naïve
Bayes (NB) were applied to a dataset of financial transactions using R programming language. As a result of the
study, when the performance of the classification techniques was compared, it was found that the DT technique made
72 % correct classification.