Customer Churn Prediction Model
Client: Telecom Giant
Designed and deployed a sophisticated machine learning model using Python, Scikit-learn, and TensorFlow to predict customer churn with high accuracy. The model analyzes call data records, customer service interactions, and billing history to identify at-risk customers, allowing the client to launch targeted retention campaigns.
November 20, 2022
Deployed model as a REST API endpoint for internal use.
October 15, 2022
Model training and validation (XGBoost vs. LSTM).
September 1, 2022
Data exploration and feature engineering.
Outcome
"Achieved 85% accuracy in churn prediction, leading to a 15% reduction in customer attrition over two quarters."
Client Feedback
""The model has given our marketing team a powerful tool. The insights are actionable and have delivered measurable results.""
Charlie Brown
Principal Data Scientist