🍱 Food Demand Forecasting using XGBoost

IBM Datathon Hackasours — Predicting Daily Food Demand for Smart Kitchens

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📘 Overview

This project implements a Machine Learning–powered forecasting system that predicts daily food demand (units sold) for multiple restaurant outlets.
It is designed to help **large-scale kitchens and welfare canteensand restaurants ** make data-driven decisions for food preparation, procurement, and distribution.

By accurately forecasting demand, the system aims to:

• Reduce food wastage 🥦
• Prevent shortages 🍛
• Optimize inventory and procurement 🧾

The pipeline integrates XGBoost within a Scikit-learn pipeline, ensuring modularity, reproducibility, and safety against data leakage.

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#CodingInLinuxOne

ML Demo

LightBGM

Insights of LightBGM

XGBOOST

LightBGM Prediction

XGBOOST

#Frontend Demo

Contribution

• Shiva Ganesh V
• Shreyas K
• Sowmya Anand
• Vishal S
• Yukesh D