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