Projects / SuperKart
Machine Learning / Deployment · Retail Analytics

SuperKart

Developed an end-to-end retail sales forecasting workflow spanning feature preparation, regression modeling, tuning, API packaging, containerization, and interactive delivery.

Business problem

Why this project matters

Developed an end-to-end retail sales forecasting workflow spanning feature preparation, regression modeling, tuning, API packaging, containerization, and interactive delivery.

Project scope

What the work covered

01Explore product, outlet, and sales variables
02Prepare features and handle missing values
03Compare regression models and tune the selected approach
04Package inference behind a Flask API
05Containerize and present the workflow through Streamlit
Workflow

From question to evidence

Business problem
→
Data preparation
→
EDA / feature work
→
Model or analysis
→
Evaluation
→
Business conclusion
Analysis dashboard

Portfolio evidence areas

These bars summarize the emphasis of the completed work; detailed numeric outputs and full notebook cells remain available in the linked repository and HTML report.

EDA
84%
Modeling
72%
Packaging
91%
Delivery
78%
Notebook evidence

Selected analysis and report visuals

These original project diagrams illustrate the technical work. Review the linked repositories for complete notebooks and supporting materials.

Conclusions

What the analysis demonstrated

Product and outlet attributes provide distinct signals for sales estimation.
A reproducible preprocessing pipeline is essential for consistent inference.
Deployment assets turn a notebook model into a usable decision-support workflow.
GitHub README preview

Repository at a glance

superkart-sales-forecasting

Retail forecasting and delivery workflow with model development, API packaging, Docker, and interactive application assets.

READMENotebook / reportSource filesProject visuals
Next step

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Use the project inquiry form with SuperKart preselected. Include the business objective, available data, timeline, and desired output.