References
- 1. Ali, J., Khan, R., Ahmad, N. and Maqsood, I. (2012). Random forests and decision trees. International Journal of Computer Science Issues (IJCSI), 9(5), 272.
- 2. Attwal, K. P. S. and Dhiman, A. S. (2020). Exploring Data Mining Tool-Weka And Using Weka To Build And Evaluate Predictive Models. Advances and Applications in Mathematical Sciences, 6(19), 451-469.
- 3. Avand, M., Janizadeh, S., Naghibi, S. A., Pourghasemi, H. R., Khosrobeigi Bozchaloei, S. and Blaschke, T. (2019). A comparative assessment of random forest and k-nearest neighbor classifiers for gully erosion susceptibility mapping. Water, 11(10), p. 2076.10.3390/w11102076
- 4. Bumblauskas, D., Nold, H., Bumblauskas, P., and Igou, A. (2017). Big data analytics: transforming data to action. Business Process Management Journal, 703-720.10.1108/BPMJ-03-2016-0056
- 5. Çığşar, B. and Ünal, D. (2019). Comparison of data mining classification algorithms determining the default risk. Scientific Programming, 2019.10.1155/2019/8706505
- 6. Durugbo, C. M. (2020). After-sales services and aftermarket support: a systematic review, theory and future research directions. International Journal of Production Research, 58(6), 1857-1892.10.1080/00207543.2019.1693655
- 7. Esmaeilpour, M. (2016). Analyzing after Sales Services in House Appliances Products and Measuring Customers Satisfaction: A Survey in Bushehr, Iran. Journal of Harmonized Research in Management, 2(2), 204-215.
- 8. Gimpel, H., Hosseini, S., Huber, R. X. R., Probst, L., Röglinger, M. and Faisst, U. (2018). Structuring Digital Transformation: A Framework of Action Fields and its Application at ZEISS. Journal of Information Technology, Theory and Application, 1(19), p. 3.
- 9. Halimu, C., Kasem, A. and Newaz, S. S. (2019, January). Empirical comparison of area under ROC curve (AUC) and Mathew correlation coefficient (MCC) for evaluating machine learning algorithms on imbalanced datasets for binary classification. In Proceedings of the 3rd international conference on machine learning and soft computing (pp. 1-6).10.1145/3310986.3311023
- 10. Hossin, M. and Sulaiman, M. N. (2015). A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 1.10.5121/ijdkp.2015.5201
- 11. Jadhav, S. D. and Channe, H. P. (2016). Comparative study of K-NN, naive Bayes and decision tree classification techniques. International Journal of Science and Research (IJSR), 5(1), 1842-1845.10.21275/v5i1.NOV153131
- 12. Kaur, G., & Chhabra, A. (2014). Improved J48 classification algorithm for the prediction of diabetes. International journal of computer applications, 98(22).10.5120/17314-7433
- 13. Krstic, J., Jovanov, G., Radovanovic, R., Ljusic, M. and Nikolic, M. (2016). Process of Business Reengineering from the Aspect of E-Business. Journal of Textile Science & Engineering, 272(6), p. 2.
- 14. Murali, S., Pugazhendhi, S. and Muralidharan, C. (2016). Modelling and investigating the relationship of after sales service quality with customer satisfaction, retention and loyalty–a case study of home appliances business. Journal of retailing and consumer services, 30, 67-83.10.1016/j.jretconser.2016.01.001
- 15. OECD and the United Kingdom Department for Business, Energy and Industrial Strategy (BEIS). (2018). Implications of the Digital Transformation for the Business. http://www.oecd.org/sti/ind/digital-transformation-business-sector-summary.pdf.
- 16. Palmer, A., Jiménez, R. and Gervilla, E. (2011). Data mining: Machine learning and statistical techniques. Knowledge-Oriented Applications in Data Mining, Prof. Kimito Funatsu (Ed.), 373-396.10.5772/13621
- 17. Ragab, A. H. M., Noaman, A. Y., Al-Ghamdi, A. S. and Madbouly, A. I. (2014, June). A comparative analysis of classification algorithms for students college enrollment approval using data mining. In Proceedings of the 2014 Workshop on Interaction Design in Educational Environments (pp. 106-113).10.1145/2643604.2643631
- 18. Ray, S. (2019, February). A quick review of machine learning algorithms. In 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 35-39). IEEE.10.1109/COMITCon.2019.8862451
- 19. Rudnick, M., Riezebos, J., Powell, D. J. and Hauptvogel, A. (2020). Effective after-sales services through the lean servitization canvas. International Journal of Lean Six Sigma, 5(11), 943-956.10.1108/IJLSS-07-2017-0082
- 20. Saputra, M. F. A., Widiyaningtyas, T. and Wibawa, A. P. (2018). Illiteracy classification using K means-Naïve Bayes algorithm. JOIV: International Journal on Informatics Visualization, 2(3), 153-158.10.30630/joiv.2.3.129
- 21. Schwertner, K. (2017). Digital transformation of business. Trakia Journal of Sciences, 15(1), 388-393.10.15547/tjs.2017.s.01.065
- 22. Sethi, S., Malhotra, D., and Verma, N. (2016). Data mining: current applications & trends. International Journal of Innovations in Engineering and Technology, 6(4), 586-589.
- 23. Verma, A. (2019). Evaluation of classification algorithms with solutions to class imbalance problem on bank marketing dataset using WEKA. International Research Journal of Engineering and Technology, 5(13), 54-60.
- 24. West, D. M. and Allen, J. R. (2018). How artificial intelligence is transforming the world. Report, Brookings Institution.
- 25. Zhu, C., Idemudia, C. U. and Feng, W. (2019). Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques. Informatics in Medicine Unlocked, 17, 100179.10.1016/j.imu.2019.100179