Customer complaint prediction of fiber internet

In this study, fiber internet customer complaints data received by a company operating in the telecommunications sector were anonymized within the scope of the personal data protection law. By modeling the machine learning algorithms decision trees, naive bayes, random forest, logistic regression and xgboost methods with the python programming language, it is ensured that customer complaints are predicted by looking at whether they really come from a problem. In the study, success rates were calculated by first creating single models with machine learning algorithms. Then, combined (binary) hybrid models were created from these algorithms and their success rates were compared. The aim of the study is to ensure rapid response to customer complaints. In addition, it is to adopt a customer-oriented approach by increasing customer satisfaction.