This project focuses on predicting customer churn using real-world telecom data. The objective is to identify customers who are likely to leave a service, enabling organizations to take proactive retention actions and make data-driven decisions.
The project emphasizes both analytical thinking and practical modeling, showing how machine learning can support customer retention strategies.
Customer churn is a critical challenge for subscription-based businesses, as retaining existing customers is often more cost-effective than acquiring new ones. However, identifying at-risk customers requires analyzing large volumes of behavioral and usage data.
This project frames churn prediction as a decision support problem, helping businesses prioritize retention efforts based on data-driven insights.
- Performed data cleaning and preprocessing on telecom customer data
- Conducted exploratory analysis to understand churn patterns
- Built and evaluated machine learning models to predict churn
- Compared model performance using appropriate evaluation metrics
- Interpreted results with a focus on business relevance
- Python
- pandas, NumPy
- scikit-learn
- Machine learning classification techniques
- Identified key patterns associated with customer churn
- Built predictive models to classify churn risk
- Demonstrated how churn predictions can support retention decisions
- Highlighted the role of analytics in reducing customer attrition
- Address class imbalance using advanced sampling techniques
- Improve model explainability for business stakeholders
- Extend the solution with deployment-ready pipelines