Open AccessImplementing Random Forest to Predict Churn
Author(s) -
Linda Makurumidze,
Wellington Simbarashe Manjoro,
Wellington Makondo
Publication year - 2022
Publication title -
international journal of computer science and mobile computing
Language(s) - English
Resource type - Journals
ISSN - 2320-088X
DOI - 10.47760/ijcsmc.2022.v11i02.009
Subject(s) - random forest , adaboost , service (business) , computer science , decision tree , tree (set theory) , marketing , business , machine learning , support vector machine , mathematics , mathematical analysis
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.