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

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

Fatma Önay Koçoğlu, Şakir Esnaf
Copyright: © 2022 |Volume: 9 |Issue: 5 |Pages: 18
ISSN: 2334-4547|EISSN: 2334-4555|EISBN13: 9781668458709|DOI: 10.4018/IJBAN.298014
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MLA

Koçoğlu, Fatma Önay, and Şakir Esnaf. "Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification." IJBAN vol.9, no.5 2022: pp.1-18. http://doi.org/10.4018/IJBAN.298014

APA

Koçoğlu, F. Ö. & Esnaf, Ş. (2022). Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification. International Journal of Business Analytics (IJBAN), 9(5), 1-18. http://doi.org/10.4018/IJBAN.298014

Chicago

Koçoğlu, Fatma Önay, and Şakir Esnaf. "Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification," International Journal of Business Analytics (IJBAN) 9, no.5: 1-18. http://doi.org/10.4018/IJBAN.298014

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Abstract

Up to the present, various methods such as Data Mining, Machine Learning, and Artificial Intelligence have been used to get the best assess from huge and important data resource. Deep Learning, one of these methods, is 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 algorithm. Naïve 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 have 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.