cutomer-churn

End-to-end Python & SQL churn analysis pipeline built on 1,000+ customer records. Features database integration, exploratory data analysis, and stakeholder-ready Matplotlib/Seaborn visualizations uncovering revenue risk and key retention drivers.

This project focuses on analyzing, understanding, and predicting customer churn in a telecom company using data analysis, visualization, clustering, and machine learning models. The goal is to identify key factors that cause customers to leave and build predictive models to help improve customer retention.

Interactive Customer Churn Prediction Dashboard using Random Forest, Streamlit, Plotly and Scikit-learn for customer retention analytics and business intelligence.

End-to-End Machine Learning project for predicting telecom customer churn using XGBoost with an interactive Streamlit web application.