Deployed app: https://bankcustomerchurnprediction-t2xuoxltd6.streamlit.app/
Kaggle notebook: https://www.kaggle.com/code/navanitnandakumar/bank-customer-churn-analysis-prediction-87
Churn prediction is the process of identifying which consumers are most likely to stop using a service or cancel their subscription. This is a critical factor for many businesses because acquiring new consumers costs more than retaining existing ones.
The banking sector is among the industries recording the largest rate of customer churn every year. One of the main causes of client attrition for retail banking organizations is the increasing market rivalry, which gives customers more options and better deals. Banks need to get a comprehensive, 360-degree view of their client base and their interactions across several channels in order to spot early indications of impending customer turnover. They would be able to identify early warning signals of customer churn, such as decreasing transactions, or bad experiences, and they could then take particular preventative measures.
This project focuses on the analysis and prediction of customer attrition for 'ABC Multinational Bank', a fictional bank using customer data. The dataset for ABC Multinational Bank indicates which customers have left, stayed, or signed up for their service. Multiple important demographics are included for each customer that can help us identify at-risk customers, pain points, and actions to be taken.