Superstore Analytics
Superstore Dashboard
Superstore Analytics
Superstore Analytics

Data-Driven Machine Learning Based Superstore Retail Optimization

This project was developed for one of our retail superstore clients to modernize their operations and decision-making process. By combining advanced data engineering, custom machine learning models, and interactive dashboards, we enabled the client to gain actionable insights into customer behavior, sales trends, and inventory management. The platform delivers real-time analytics, predictive forecasts, and automated reporting, ensuring operational efficiency and smarter business decisions across multiple store locations.

Project Highlights

  • Data Platform: Leveraged Microsoft Fabric Lakehouse to store, organize, and manage both raw and cleaned data.
  • Machine Learning Models: Using PySpark, Pandas, and Scikit-learn, we developed ML models to segment customers, identify buying patterns, and predict sales trends.
  • Interactive Dashboard: Designed dynamic Power BI reports exploring performance by category, geography, and customer type, including a summary report page.
  • End-to-End Automation: Implemented pipelines to refresh datasets, clean data, run ML models, update scores, and automatically refresh the dashboard in real-time.

Business Impact

  • Customer Insights: Segmented customers into meaningful groups to improve targeting and retention strategies.
  • Revenue Forecasting: Built predictive models to anticipate sales trends, improving inventory management.
  • Category & Regional Analysis: Identified top-performing categories and locations to optimize pricing, promotions, and resource allocation.
  • Operational Efficiency: Automated workflows reduced manual reporting, saving time and ensuring accuracy.

Tools & Technologies

  • Microsoft Fabric: Lakehouse, Dataflows Gen2, Pipeline, Notebook
  • Python: PySpark, Pandas, Scikit-learn
  • Power BI: Data visualization and storytelling

Project Information

  • Client: RetailMart
  • Portfolio: Power BI
  • Service: Dashboard Development
  • Category: Retail
  • Date: 11 Mar 2024