github.com/Arnesh-26/supply_packaging @main

📦 Packaging Prediction in Supply Chain 📌 Overview

This project focuses on predicting the optimal packaging type (Glass or Plastic) in a supply chain using environmental and logistical features. A Random Forest classification model is trained to assist organizations in making data-driven packaging decisions, improving cost efficiency, sustainability, and operational reliability.

🎯 Problem Statement

Choosing the right packaging material is critical in supply chains, as it impacts:

Product safety

Transportation costs

Environmental sustainability

Damage and breakage rates

Traditional decision-making methods rely on heuristics. This project applies machine learning to automate and optimize packaging selection.

🧠 Solution Approach

Preprocessed supply chain data containing environmental and logistics-related attributes

Applied Random Forest Classifier to capture non-linear relationships

Evaluated model performance using standard classification metrics

Extracted insights to support supply chain optimization

📊 Features Used

Transportation distance

Temperature conditions

Humidity levels

Fragility index

Weight of shipment

Handling complexity

Environmental exposure factors

⚙️ Tech Stack

Programming Language: Python

Libraries & Tools:

NumPy

Pandas

Scikit-learn

Matplotlib / Seaborn

Model: Random Forest Classifier

🚀 Workflow

Data collection and cleaning

Feature engineering and encoding

Train-test split

Model training using Random Forest

Performance evaluation

Insight extraction for decision-making

📈 Model Performance

Achieved high classification accuracy

Robust against overfitting due to ensemble learning

Performs well on unseen data

Evaluation Metrics:

Accuracy

Precision

Recall

F1-score

🔍 Key Insights

Environmental factors strongly influence packaging choice

Glass packaging is preferred under controlled conditions

Plastic packaging is more suitable for longer distances and high handling risk

Random Forest effectively captures feature interactions

🌱 Future Enhancements

Add real-time prediction via REST API

Incorporate cost and carbon footprint metrics

Try advanced models (XGBoost, LightGBM)

Deploy using Flask or FastAPI

🤝 Contribution

Contributions are welcome! Feel free to fork the repository and submit pull requests.