Customer churn is one of the most costly problems faced by subscription-based businesses. Retaining an existing customer is often significantly cheaper than acquiring a new one, making early churn detection a high-impact use case for machine learning.
This project applies applied machine learning techniques to predict customer churn and, more importantly, to understand why customers leave. The emphasis is not just on predictive accuracy, but on building a robust, reproducible, and interpretable end-to-end data science pipeline that reflects real-world practice.
The main goals of this project are to:
- Predict whether a customer is likely to churn using supervised machine learning
- Compare multiple classification models under a fair and consistent evaluation framework
- Translate model outputs into actionable business insights that can support retention strategies
The dataset consists of customer demographic information, account details, and service usage patterns, alongside a binary churn indicator.
Key characteristics of the data include:
- A mix of numerical and categorical features
- Class imbalance, with fewer churned customers relative to retained ones
- Realistic noise and feature correlations commonly observed in business datasets
These properties make the dataset well-suited for demonstrating applied machine learning challenges.
- Inspected dataset structure, missing values, and target distribution
- Implemented preprocessing using scikit-learn Pipelines and ColumnTransformer
- Ensured all transformations were fit exclusively on training data to prevent data leakage
- Examined feature distributions and relationships
- Analyzed correlations and churn patterns
- Identified early signals and variables associated with increased churn risk
Multiple classification models were trained using identical preprocessing pipelines to ensure an unbiased comparison.
Key decisions included:
- Selecting ROC-AUC as the primary evaluation metric due to class imbalance
- Evaluating all models on unseen test data
- Using comparative performance to guide final model selection
- Predicted probabilities were used instead of fixed class labels
- Decision thresholds were tuned to balance recall and precision
- Business cost considerations were incorporated, with particular attention to the cost of missing high-risk churn customers
- Feature importance analysis was conducted
- Key drivers of customer churn were identified
- Model results were translated into clear, business-friendly insights
- The selected model demonstrated strong discriminatory power in separating churned and retained customers
- Several service usage and account-related features emerged as strong predictors of churn
- Threshold tuning significantly improved the model’s ability to identify high-risk customers
The resulting model can support:
- Proactive churn prevention strategies
- Targeted customer retention and engagement campaigns
- Data-driven decision-making for customer management teams
While the model performs well, several limitations remain:
- External behavioral and market-level factors were not included
- Customer behavior was modeled as static rather than time-dependent
Potential future improvements include:
- Cost-sensitive learning approaches
- Time-based or sequential churn modeling
- Validation using real-time or external datasets
- Python
- pandas, NumPy
- scikit-learn
- matplotlib, seaborn
- Jupyter Notebook
GitHub: https://github.com/RackLabz
LinkedIn: https://www.linkedin.com/in/shedrack-chinonso-69058219a