🌦️ Weather Prediction Using Numerical Methods

This project utilizes numerical methods and machine learning to predict weather conditions based on historical meteorological data. By leveraging data from Meteostat and solar position calculations from Astral, the system forecasts weather patterns with a data-driven approach.

🚀 Features

• ✅ Real-Time Weather Data – Uses Meteostat to fetch historical and real-time weather information.
• ✅ Numerical Methods – Applies statistical techniques and models for predictions.
• ✅ Astronomical Data Integration – Calculates sunrise, sunset, and other solar parameters.
• ✅ Data Processing & Analysis – Cleans and structures meteorological data using numpy and pandas.

📦 Installation

Ensure you have Python installed, then install the required dependencies:

pip install --upgrade meteostat numpy pandas astral

📊 Data Sources

• Meteostat API – Fetches historical and real-time weather data.
• Astral Library – Computes sun position and daylight durations.

📈 Future Improvements

• Implement machine learning models for improved accuracy.
• Add time-series forecasting (e.g., ARIMA, LSTMs).
• Deploy as a web application for easy access.

📜 License

This project is licensed under the MIT License.