DRIVER’S CHURN PREDICTION AND PREVENTION IN RIDE HAILING INDUSTRY USING PREDICTIVE MODELING TECHNIQUES

Abstract

Churn Prediction uses data mining techniques to facilitate companies in consumer retention. The purpose of this thesis research is to study prediction classifiers used in data mining and apply them in case of ride-hailing industry to form Driver Churn Prediction Model. Subscription-based companies in sectors like banking, media uses it to know the customer behaviour so that they can serve them better and prevent the churn. Companies spent a lot of time and money to acquire the customer and they need to spend again on marketing to get a customer back or to get a new one. All companies are interested that their customer should not leave and remain loyal to them. To achieve this loyalty company needs to work on data and predict the churn and compensate them. In this research study I have developed churn prediction model based on previously available data. Transportation Network Providers dataset is used to train a Driver Churn Prediction Model to predict driver churn. For prediction techniques such as logistic regression, decision tree, random forest, Bayesian optimization and Neural networks are fused together to generate a model to derive a model for churn prediction. The accuracy score and Confusion matrix of each classifier is compared to show better performing model. The outcomes gathered in this study also show the significance of class imbalance problem, which is solved using SMOTE, in case of churn prediction.