Implementing Random Forest to Predict Churn

For one to remain afloat in business, the best marketing technique is to maintain the current customers rather getting new ones [1]. [2] Shows that it costs more to get a fresh client than maintaining the available ones. An organization that intends to keep its customers must speculate which of them is at risk of abandoning their service and put all their concentration on those customers in an effort to retain them. This paper's contribution is to create a prototype, which aid banks to foretell clients that are prone to abandon their service. This paper makes use of four algorithms namely Gradient boost, Random forest, Adaboost and Decision tree to classify and segment bank clients based on a number of features. The paper then selects the best performing algorithm, that is Random forest , to build a prediction model that can used by banks to identify the most likely clients to churn away.