I will build a customer churn prediction model using python

Stop guessing why your customers are leaving.

I help businesses turn raw customer data into actionable retention strategies using Python, statistics, and machine learning.

Instead of chasing accuracy alone, my focus is on reliable analysis and real business impact.

In a recent project, I built a churn prediction pipeline combined with a retention budget simulation, identifying 593 priority customers and projecting scenario-based ROI between 1.02× and 7.08×.

How we can work together

• Churn Analysis & Prediction: Identify at-risk customers and understand key churn drivers using Logistic Regression and XGBoost.
• Beyond Accuracy: Evaluate models using precision, recall, ROC-AUC, and PR-AUC.
• Customer Value & Retention: Use survival analysis to estimate churn timing and RFM segmentation to prioritize retention efforts.
• Clean, Reproducible Deliverables: Python code, SQL scripts, Streamlit apps, visualizations, or executive-ready reports.

Let's talk before you book: Send me a brief description of your dataset and business goal. I'll review the scope and suggest the most appropriate analytical approach.

I usually respond within a few hours.