Food Sales Prediction Model using scikit-learn* (sklearn): Developer Spotlight

Ramya_Ravi · ‎09-11-2023

Sales prediction is important in the food industry to improve business operations and profitability. Also, sales prediction allows companies to minimize stocked and expired products and, at the same time, avoid missing sales.

Sayan Malakar is an entrepreneur and co-founder of Try the menu, a startup that provides engagement and management services to restaurants and cafes. Sayan, in his blog, proposed a solution for food sales prediction using Intel® Extension for Scikit-learn* and Streamlit. With this prediction model, he can help his clients make better business decisions by enabling them to introduce new menu items and pricing strategies based on predicted sales.

The blog explains the various steps involved in the project:

Setup the environment for the project
Data Preprocessing
Model Training
Model Testing

Read more about the project on Medium and GitHub.

Learn more about Intel Extension for Scikit-learn!

Intel Extension for Scikit-learn: This Intel Extension seamlessly speeds up your scikit-learn applications for Intel architectures across single and multi-node configurations. Also, this extension package dynamically patches scikit-learn estimators while improving performance for your machine learning algorithms. By using Scikit-learn with this extension, the performance for training and inference can be improved up to 100x with the equivalent mathematical accuracy.

Download Intel Extension for Scikit-learn as part of the Intel® AI Analytics Toolkit (AI Kit).

What’s Next?

We encourage you to check out and incorporate Intel’s other AI/ML Framework optimizations and end-to-end portfolio of tools into your AI workflow and learn about the unified, open, standards-based oneAPI programming model that forms the foundation of Intel’s AI Software Portfolio to help you prepare, build, deploy, and scale your AI solutions.

For more details about the new 4th Gen Intel® Xeon® Scalable processors, visit Intel's AI Solution Platform portal to learn how Intel empowers developers to run end-to-end AI pipelines on these powerful CPUs.

juliawhites · ‎09-19-2026

This is a really interesting example of using machine learning for a practical business problem. Accurate sales forecasting can make a big difference in reducing food waste while helping restaurants plan inventory and pricing more effectively. I also like that the project combines Scikit-learn with Streamlit to make the model more accessible.

Has anyone here tried using similar ML models for sales forecasting in another industry?

ThomasClark · ‎09-23-2026

Hey,
One interesting part of food sales prediction is how much context can be hidden behind a simple sales number. An item may sell more because of its price, category, time of day, season, or even because it is bundled with another product. A model built with scikit-learn could use those variables to uncover patterns and make future demand estimates more useful for inventory and menu planning.