Details

Autor(en) / Beteiligte

• Varol Malkocoglu, Ayse Berika
• Utku Malkocoglu, Sevki

Titel

Comparative Performance Analysis of Random Forest and Logistic Regression Algorithms

Ist Teil von

• 2020 5th International Conference on Computer Science and Engineering (UBMK), 2020, p.25-30

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Quelle

IEL

Beschreibungen/Notizen

• Today, banks are trying to meet the needs of their existing customers with the marketing activities they do in digital media. It is known to produce statistical results in order to be able to predict the behavior of customers in artificial intelligence applications by storing large-scale data obtained through marketing studies. In this study, performance comparison between random forest and logistic regression algorithms was made by using real banking marketing data that includes the characteristics of customers. In addition, these algorithms were run on WEKA, Google Colab and MATLAB platforms to compare performance on different platforms. At the end of the study, the most successful result obtained with 94.8% accuracy, 93.9% sensitivity, 94.8% recall, 94.4% fl-score and 98.7% AUC value was achieved by random forest algorithm on WEKA platform. In addition, it has been shown that the obtained performance values produce better results compared to similar studies.