FoodBridge AI
Intelligent Global Food Waste Redistribution System
FoodBridge AI is a machine learning system designed to predict food surplus and identify waste levels in food service environments. The system helps reduce food waste by enabling smarter redistribution strategies.
Project Objectives
The goal of this project is to:
- Predict food surplus using machine learning
- Classify waste levels (Low / Medium / High)
- Provide real-time predictions through APIs
- Offer an interactive dashboard for users
- Support data-driven food redistribution
Project Architecture
FoodBridge AI consists of the following components:
- Exploratory Data Analysis (EDA)
- Machine Learning Models
- Model Training Pipelines
- FastAPI Backend
- Streamlit Dashboard
- Model Documentation
Project Structure
FoodBridge AI
Intelligent Global Food Waste Redistribution System
FoodBridge AI is a machine learning system designed to predict food surplus and identify waste levels in food service environments. The system helps reduce food waste by enabling smarter redistribution strategies.
Project Objectives
The goal of this project is to:
- Predict food surplus using machine learning
- Classify waste levels (Low / Medium / High)
- Provide real-time predictions through APIs
- Offer an interactive dashboard for users
- Support data-driven food redistribution
Project Architecture
FoodBridge AI consists of the following components:
- Exploratory Data Analysis (EDA)
- Machine Learning Models
- Model Training Pipelines
- FastAPI Backend
- Streamlit Dashboard
- Model Documentation
Project Structure
FoodBridge_AI │ ├── data │ ├── notebooks │ ├── 01_EDA.ipynb │ ├── 02_surplus_prediction_model.ipynb │ └── 03_waste_classification_model.ipynb │ ├── utils │ ├── models │ ├── api │ └── main.py │ ├── dashboard │ └── app.py │ ├── saved_models │ ├── foodbridge_regressor.pkl │ ├── waste_classifier.pkl │ ├── model_features.json │ └── classifier_features.json │ ├── model_cards │ └── foodbridge_model_card.md │ └── README.md
Dataset
The dataset contains approximately 10,000 records collected from various food service environments.
Key attributes include:
- food preparation quantity
- customer footfall
- demand indicators
- environmental factors
- location information
Machine Learning Models
Surplus Prediction
Algorithm used:
Random Forest Regressor
Performance:
MAE ≈ 1.7
R² ≈ 0.99
Waste Classification
Algorithm used:
Random Forest Classifier
Performance:
Accuracy ≈ 90%
Technologies Used
- Python
- Pandas
- Scikit-Learn
- FastAPI
- Streamlit
- Matplotlib
- Seaborn
Running the Project
1 Install Dependencies
Dataset
The dataset contains approximately 10,000 records collected from various food service environments.
Key attributes include:
- food preparation quantity
- customer footfall
- demand indicators
- environmental factors
- location information
Machine Learning Models
Surplus Prediction
Algorithm used:
Random Forest Regressor
Performance:
MAE ≈ 1.7
R² ≈ 0.99
Waste Classification
Algorithm used:
Random Forest Classifier
Performance:
Accuracy ≈ 90%
Technologies Used
- Python
- Pandas
- Scikit-Learn
- FastAPI
- Streamlit
- Matplotlib
- Seaborn
Running the Project
1 Install Dependencies
pip install -r requirements.txt
2 Run FastAPI Server
cd api
uvicorn main:app --reload
API documentation will be available at:
http://127.0.0.1:8000/docs
3 Run Streamlit Dashboard
cd dashboard
streamlit run app.py
Dashboard will open at:
http://localhost:8501
Applications
FoodBridge AI can be used by:
- restaurants
- supermarkets
- NGOs
- food banks
- smart city initiatives
Future Enhancements
- Real-time IoT food monitoring
- Deep learning demand forecasting
- NGO logistics integration
- Geographic redistribution optimization
Author
Final Project
FoodBridge AI – Intelligent Food Waste Prediction System