Churn Analysis

Problem

Customer churn directly impacts recurring revenue, but many teams discover churn risk too late. Without early warning indicators, outreach efforts are reactive instead of preventative, and retention resources are spent inefficiently.

Approach

I built an end-to-end ML workflow: data profiling, missing-value strategy, feature engineering, exploratory analysis, and iterative model evaluation. I prioritized interpretability alongside performance so non-technical stakeholders could trust and act on the results.

Key Highlights

• Built a full data pipeline for cleaning, feature engineering, and model-ready transformation.
• Compared multiple baseline models and documented evaluation tradeoffs for business use.
• Focused outputs on actionable retention insights rather than raw model metrics only.

Lessons Learned

• Explainability accelerated stakeholder trust more than marginal metric gains.
• Data quality checks early in the pipeline prevented costly rework later.
• Clear business framing kept modeling decisions aligned with measurable impact.

Tech Stack

• Python
• Pandas
• Scikit-Learn
• Jupyter

Outcome

The project produced a reusable churn prediction pipeline and clear risk segmentation outputs that can support retention campaigns, account prioritization, and future experimentation on intervention impact.