foodbridge-regression-model

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.

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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

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Project Architecture

FoodBridge AI consists of the following components:

1. Exploratory Data Analysis (EDA)
2. Machine Learning Models
3. Model Training Pipelines
4. FastAPI Backend
5. Streamlit Dashboard
6. Model Documentation

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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:

1. Exploratory Data Analysis (EDA)
2. Machine Learning Models
3. Model Training Pipelines
4. FastAPI Backend
5. Streamlit Dashboard
6. Model Documentation

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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

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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

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Machine Learning Models

Surplus Prediction

Algorithm used:

Random Forest Regressor

Performance:

MAE ≈ 1.7
R² ≈ 0.99

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Waste Classification

Algorithm used:

Random Forest Classifier

Performance:

Accuracy ≈ 90%

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Technologies Used

• Python
• Pandas
• Scikit-Learn
• FastAPI
• Streamlit
• Matplotlib
• Seaborn

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Running the Project

1 Install Dependencies

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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

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Running the Project

1 Install Dependencies

pip install -r requirements.txt

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2 Run FastAPI Server

cd api
uvicorn main:app --reload

API documentation will be available at:

http://127.0.0.1:8000/docs

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3 Run Streamlit Dashboard

cd dashboard
streamlit run app.py

Dashboard will open at:

http://localhost:8501

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Applications

FoodBridge AI can be used by:

• restaurants
• supermarkets
• NGOs
• food banks
• smart city initiatives

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Future Enhancements

• Real-time IoT food monitoring
• Deep learning demand forecasting
• NGO logistics integration
• Geographic redistribution optimization

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Author

Final Project

FoodBridge AI – Intelligent Food Waste Prediction System