Churn Prediction using Various Machine Learning Algorithms

In the era of big data, customer churn is a big problem faced by banks in the increasingly competitive market. The number of service providers are being increased very rapidly in every business. In these days, there is no shortage of options for customers in the banking sector when choosing where to put their money. In this paper, a method to predicts the customer churn in a Bank, using machine learning techniques, which is a branch of artificial intelligence is proposed. The research promotes the exploration of the likelihood of churn by analyzing customer behaviour. The KNN, SVM, Decision Tree, and Random Forest classifiers are used in this study. Also, some feature selection methods have been done to find the more relevant features and to verify system performance. The experimentation was conducted on the churn modelling dataset from Kaggle. The result gives us that in which algorithm the customer will stay or exits according to the data.