  Data transformation (DT) is a process that transfers the original data into a
form which supports a particular classification algorithm and helps to analyze
the data for a special purpose. To improve the prediction performance we
investigated various data transform methods. This study is conducted in a
customer churn prediction (CCP) context in the telecommunication industry
(TCI), where customer attrition is a common phenomenon. We have proposed a
novel approach of combining data transformation methods with the machine
learning models for the CCP problem. We conducted our experiments on publicly
available TCI datasets and assessed the performance in terms of the widely used
evaluation measures (e.g. AUC, precision, recall, and F-measure). In this
study, we presented comprehensive comparisons to affirm the effect of the
transformation methods. The comparison results and statistical test proved that
most of the proposed data transformation based optimized models improve the
performance of CCP significantly. Overall, an efficient and optimized CCP model
for the telecommunication industry has been presented through this manuscript.
