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.
Notebook evidence
Selected analysis and report visuals
Notebook / HTML report snapshot 1
Notebook / HTML report snapshot 2
Notebook / HTML report snapshot 3
Notebook / HTML report snapshot 4
Notebook / HTML report snapshot 5
Notebook / HTML report snapshot 6
Images are loaded from the public project repository so the portfolio stays synchronized with the published notebook/report assets.
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.
Next step
Discuss a similar project
Use the project inquiry form with SuperKart preselected. Include the business objective, available data, timeline, and desired output.