Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …

A thorough review has been performed on interpolation methods to fill gaps in time-series,
efficiency criteria, and uncertainty quantifications. On one hand, there are numerous …

Decision trees and logistic regression are two very popular algorithms in customer churn
prediction with strong predictive performance and good comprehensibility. Despite these …

Customer retention campaigns increasingly rely on predictive analytics to identify potential
churners in a customer base. Traditionally, customer churn prediction was dependent on …

Customer churn prediction methods aim to identify customers with the highest probability of
attrition, improve the effectiveness of customer retention campaigns, and maximize profits …

Companies in a variety of sectors are increasingly managing customer churn proactively,
generally by detecting customers at the highest risk of churning and targeting retention …

Data preparation is a process that aims to convert independent (categorical and continuous)
variables into a form appropriate for further analysis. We examine data-preparation …

Predicting future customer behavior provides key information for efficiently directing
resources at sales and marketing departments. Such information supports planning the …

Many organizations recognize the necessities of utilizing sophisticated tools and systems to
protect their computer networks and reduce the risk of compromising their information …

Customer churn is one of the most challenging problems that affects revenue and customer
base in mobile telecom operators. The success of retention campaigns depends not only on …