Churn Prediction in Cloud with Fuzzy Boosted Trees

Churn prediction is a biggest concern for an organization due to its associated costs. The aim of customer churn prediction is detecting customers with high tendency to leave a company. Although, many modeling techniques have been used in the field of churn prediction, performance of ensemble methods has not been thoroughly investigated yet. With the rapid growth of digital systems and associated information technologies, there is an emerging trend in the global economy to build digital customer relationship management (CRM) systems due to which consideration in churn prediction has proven promising. As the number of suitable classification methods increases, it has become more difficult to assess which one is the most effective for our application and which parameters to use for its validation. Earlier, most researchers that uses boosting as a method to boost the accuracy of a given basis learner, this dissertation tries to separate customers into two clusters based on the weight assigned by the boosting algorithm. For the purpose of improving the predictive accuracy and interpret ability of churn prediction model, fuzzy Boosted Tree algorithm, which enhances the boosted tree, is proposed in this dissertation to predict customer-s churn propensities. The proposed algorithm is implemented in Net beans IDE of version 3. Data sets are used, on which Mining is applied with WEKA tool. Experimental evaluation reveals that Fuzzy boosting algorithm also provides a good separation of churn data, thus, Fuzzy boosting algorithm is suggested for churn prediction analysis. The experimental results give satisfactory results and are more pleasing and comfortable.