This project analyzes a telecom customer dataset to understand the factors driving customer churn and builds a logistic regression model to predict which customers are likely to leave. The report includes detailed exploratory data analysis (EDA), data cleaning, feature engineering, and model evaluation. Key findings highlight the impact of contract type, tenure, payment method, and monthly charges on churn risk. The model’s insights are presented with clear visualizations and actionable business recommendations to help stakeholders improve customer retention strategies. The final cleaned dataset is also exported for use in interactive Tableau dashboards. This comprehensive project demonstrates practical skills in R programming, data analysis, statistical modeling, and business storytelling—ideal for junior data analyst portfolios.