Food Demand and Waste Reduction Predictor

Inspiration: The idea for this project came from observing the food industry’s persistent problem with overproduction and wastage. Restaurants and catering services often struggle to predict daily food demand accurately, leading to either excess inventory or shortages. We wanted to create a solution that could help businesses optimize their food preparation, save costs, and reduce food waste.

What We Learned: Throughout this project, we gained hands-on experience with machine learning, data preprocessing, and cloud deployment. We learned how to work with real-world datasets, implement predictive models like XGBoost, and visualize predictions effectively. Additionally, deploying the model on AWS SageMaker taught us how to scale AI solutions for practical, real-time applications.

How We Built It:

Data Collection & Preprocessing: We gathered historical sales and demand data, cleaned it, handled missing values, and engineered relevant features.

Modeling: We experimented with different regression and tree-based algorithms, finally selecting XGBoost for its superior accuracy in predicting daily food demand.

Integration: We created a user-friendly interface to input daily parameters and display predicted demand along with actionable insights.

Deployment: The model and interface were deployed on AWS SageMaker, allowing real-time predictions and easy access for businesses.

Challenges We Faced:

Data Quality: The dataset had missing and inconsistent entries, which required careful preprocessing to ensure accurate predictions.

Model Optimization: Tuning hyperparameters for the best performance while avoiding overfitting was challenging.

Deployment: Integrating the model into a cloud platform with an accessible interface required learning AWS SageMaker and handling API endpoints.

Conclusion: This project not only enhanced our technical skills but also gave us insights into solving real-world business problems using AI. By predicting food demand accurately, we aim to help restaurants reduce waste, optimize costs, and improve operational efficiency.## Inspiration