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📌 Project Overview

Earthquake-Prediction-Model-with-Machine-Learning is a Python-based machine learning project that uses historical seismic data to build a predictive model capable of estimating the likelihood or severity of future earthquakes. The project showcases how ML techniques can be applied to geophysical data for potential early warning systems.

🧠 Key Features

  • 🔍 Data preprocessing & feature engineering for seismic datasets
  • 📊 Model training using supervised learning techniques
  • 📈 Accuracy evaluation with performance metrics
  • 📁 Clean and modular project structure

📁 Repository Structure


Earthquake-Prediction-Model-with-Machine-Learning/
├── model/
│   └── earthquake-prediction-model.py   # Main ML script
├── .gitignore                           # Python/OS-specific ignores
├── LICENSE                              # Apache 2.0 License
└── README.md                            # You're reading it!

🚀 Getting Started

✅ Prerequisites

  • Python 3.8+
  • pip (Python package manager)

📦 Install Dependencies

pip install numpy pandas scikit-learn matplotlib

⚙️ Usage

  1. Clone the repository:
git clone https://github.com/<your-username>/Earthquake-Prediction-Model-with-Machine-Learning.git
cd Earthquake-Prediction-Model-with-Machine-Learning
  1. Navigate to the model folder and run the script:
cd model
python earthquake-prediction-model.py
  1. View model performance metrics in the terminal or export results for further analysis.

📊 Model Information

  • 📌 Algorithms used: Logistic Regression / Random Forest / etc. (depending on your implementation)
  • 📁 Input: Cleaned earthquake dataset (magnitude, depth, location, etc.)
  • 📈 Output: Predicted earthquake occurrence or severity class

🧪 Example Dataset

⚠️ Dataset not included in repo. You can use publicly available seismic datasets such as:

📚 Future Improvements

  • Integrate real-time data feeds (e.g., USGS APIs)
  • Improve feature selection using geospatial correlations
  • Deploy the model as a Flask or Streamlit API

📜 License

This project is licensed under the Apache 2.0 License – see the LICENSE file for details.

Created by nathnaelg | Powered by Python 🐍 & Machine Learning 🧠