Problem Statement:

Organizations need a systematic way to understand the likelihood of individual customers being retained and the contributing factors, to tailor retention efforts effectively

Objective:

Develop a production-grade pipeline that predicts customer retention likelihood with explainable features, supporting actionable business decisions and enhanced ROI

Process Pipeline:

• Data Ingestion from Azure Blob Storage/SQL
• Feature Engineering using Azure Databricks/Pandas
• Logistic Regression Model Training
• Deployment through Azure ML and API Management
• Continuous Monitoring with Azure ML

Technologies & Models:

Logistic Regression, Azure ML, Azure Databricks, Scikit-learn

Key Outcomes:

Explainable Features, Retention Likelihood Accuracy, ROI Enhancement