This project involves building a machine learning model using Azure Machine Learning to predict customer churn for a subscription-based business. It includes data ingestion from Azure storage, feature engineering, model training with Scikit-learn, hyperparameter tuning, and deployment as a real-time endpoint. The solution demonstrates how enterprises use predictive analytics to improve customer retention strategies.

This project focuses on developing a time-series forecasting model on Azure to predict product demand across retail stores. Learners prepare historical sales data, train regression and forecasting models, evaluate accuracy metrics, and deploy the model for batch inference. The project reflects real-world retail analytics used for inventory optimization and supply chain planning.

This project centers on creating a credit risk assessment model using Azure ML to classify loan applicants based on risk levels. It covers data preprocessing, handling class imbalance, model explainability, and responsible AI practices. The deployed solution mirrors how financial institutions automate credit decisioning in cloud environments.