Customer Churn Prediction

Project Overview:

In an era where customer loyalty is paramount, understanding the factors behind customer churn is critical for sustained business success. Our project aims to analyze customer behaviors and develop predictive models that inform retention strategies.

Key Highlights:

•Data Collection: Gathered customer data from various sources.

•Data Analysis: Used visualizations to identify trends and patterns.

•Machine Learning Models: Developed predictive models, XGBoost.

•Deployment: Created user-friendly applications with Flask and Streamlit for real-time predictions.

Results & Impact:

•Our predictive model achieves an accuracy of 95% , enabling businesses to proactively address customer churn and enhance retention strategies.

•This project showcases the transformative potential of data-driven solutions in enhancing customer relationship management and driving business growth.