Anonymized Client Engagement
In Production
Machine Learning
Customer Churn Prediction Engine
A subscription service business needed to proactively identify accounts showing high risk of churn.
Note: This engagement is presented as an anonymized case study to preserve client confidentiality.
The Challenge
A subscription service business needed to proactively identify accounts showing high risk of churn.
The Engineering Solution
Engineered customer usage features, trained gradient boosting algorithms, and integrated automated risk scoring into the team workflow.
Implementation Result
Designed a risk-scoring workflow suitable for CRM integration.
Technologies & Tools
Python
Scikit-learn
XGBoost
PostgreSQL
Docker
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