This study can determine a customer's churn based on his historical data and behavior. It
indicates that an efficient churn prediction model should employ a significant volume of
historical data to identify churners. However, existing models have several limitations that make
it difficult to do churn prediction reasonably and accurately. To solve this issue this study
proposed new combined technique of statistical filter and machine learning preprocessing is
used. Furthermore, statistical methods are utilized to generate models, resulting in poor
prediction performance. Also, benchmark datasets are not employed in the literature for model
evaluation, resulting in a poor representation of the actual visual representation of data. Without
benchmark datasets, it is impossible to compare different models fairly. An intelligent model can
be utilized to relieve current issues and deliver more accurate churn prediction. |
