Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification

Abstract

Up to the present, various methods such as data mining, machine learning, and artificial intelligence have been used to get the best assessment from huge and important data resources. Deep learning, one of these methods, is an extended version of artificial neural networks. Within the scope of this study, a model has been developed to classify the success of tele-marketing with different machine learning algorithms, especially with deep learning algorithms. Naive Bayes, C5.0, Extreme Learning Machine, and Deep Learning algorithms have been used for modelling. To examine the effect of class label distribution on model success, synthetic minority oversampling technique has been used. The results have revealed the success of deep learning and decision trees algorithms. When the data set was not balanced, the deep learning algorithm performed better in terms of sensitivity. Among all models, the best performance in terms of accuracy, precision, and F-score have been achieved with the C5.0 algorithm.