Developing an ensemble approach for predicting customer churn in telecommunication industry
Date
2022
Authors
Todoshchuk, Nazar
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Abstract
Customer satisfaction and retention are key goals and, at the same time challenges,
for most of the modern companies which try to keep up with the times. To identify
and retain the customers who are most likely to ‘break ties’ with the company, the
latter spend much financial and technological resources. Those include advanced
machine learning algorithms for customer churn prediction. This thesis explores a
number of different common ML algorithms, including logistic regression, support
vector machines, decision tree, random forest and XGBoost, which predict customer
churn in wireless telecommunication industry. To mitigate the risks of non-accurate
predictions, an ensemble algorithm is developed based on the weighted voting approach.
In this thesis the performance of ensemble algorithm will be compared to
those of all above mentioned to rank them by prediction accuracy and choose the
best-performing one.
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Citation
Todoshchuk Nazar. Developing an ensemble approach for predicting customer churn in telecommunication industry. Bachelor Thesis. Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences. Lviv 2022, 43 p.