🔍 Post-processing GLH & ML-based Location Prediction 📈🤖

Analyze. Predict. Visualize.
This project combines data post-processing and machine learning for predictive analytics.
It processes GLH (Global Load Handling) datasets and applies multiple ML models to predict location with improved accuracy.

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📚 Table of Contents

• 📌 Overview
• ✨ Features
• 🧠 Machine Learning Models
• 📂 Project Structure
• 🛠 Requirements
• ▶ How to Run
• 📊 Example Outputs
• 📌 Use Cases
• 🤝 Contributing
• 📜 License

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📌 Overview

This notebook demonstrates:

• Data Post-Processing for GLH datasets
• Feature Engineering for predictive tasks
• Model Training & Evaluation using multiple ML algorithms
• Visualization of Results with charts and metrics

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✨ Features

✔ Clean & preprocess GLH datasets
✔ Train different ML models to predict location
✔ Compare model performance using accuracy and error metrics
✔ Generate insightful visualizations for analysis
✔ Modular and easy to customize

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🧠 Machine Learning Models

The following algorithms were implemented and evaluated:

• ✅ Linear Regression
• ✅ Decision Tree
• ✅ Random Forest
• ✅ Support Vector Machine (SVM)
• ✅ XGBoost
• ✅ K-Nearest Neighbors (KNN)

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Performance metrics used:

• Accuracy
• RMSE (Root Mean Square Error)
• R² Score

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📂 Project Structure

├── Post-processing GLH.ipynb # Main notebook (data processing + ML models)

├── README.md # Project documentation

└── requirements.txt # Python dependencies

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🛠 Requirements

• Python 3.8+
• Jupyter Notebook / Jupyter Lab
• Install dependencies: pip install pandas numpy matplotlib seaborn scikit-learn xgboost

1. Clone this repository

git clone https://github.com/your-username/your-repo-name.git

2. Navigate to the project folder

cd your-repo-name

3. Open the notebook

jupyter notebook "Post-processing GLH.ipynb"

📊 Example Outputs

Here’s what you can expect:

📌 Data Visualization:

📌 Model Comparison (Accuracy):

📌 Prediction Example:

📌 Use Cases

✅ Industrial load data analysis & prediction

✅ Location prediction for engineering applications

✅ Research & academic projects in predictive modeling

🤝 Contributing

Contributions are welcome!

Fork the repo

Create a feature branch

Submit a pull request

📜 License

This project is licensed under the MIT License.