Experimental Data Analysis to Recognize and Visualise the Factors Contributing to Bank Customer Churn Prediction Using Ensemble Learning Models

Customer churn happens when a client breaks down using a specific corporation's services and goods. It distresses the profit of industries heavily on their revenues. A novel customer acquisition is valuable up to five times more than absorbing an existing one. The target of this paper comprehend and predict customer churn in the banking sector. The bank wants to create more value out of its customer data. Analyse the data and propose how internal and external utilization of the analysis results increases the bank's revenues. We focus on exploratory data analysis to recognize and visualize features causative of client churn. This analysis predicts the purpose of constructing machine models. These models perform classification tasks for the given dataset. Multiple models were tested in our present research for bank customer churn prediction. Among all models, the LGBM model predicts the highest accuracy, 82.1%.