Predicting Product Demand Using Batch-Processed Data In Azure Databricks Ml

Abstract

Accurate demand forecasting is critical for
inventory optimization, cost reduction, and
customer satisfaction in data-driven
enterprises. Traditional statistical approaches
often fall short in managing high-volume data,
handling complex seasonality, or incorporating
external factors. This project addresses these
limitations by building a scalable and
automated demand forecasting system using
Azure Databricks. Leveraging its Apache
Spark-powered batch processing engine, Delta
Lake for reliable data storage, and machine
learning frameworks like MLflow, the system
enables efficient data preprocessing, model
training, and deployment. By analyzing
historical sales data, the model predicts future
product demand with improved accuracy and
reduced manual effort, making it suitable for
modern enterprise applications in retail and
supply chain management.