🌬️ Wind Speed Prediction System

Live Demo: Wind Speed Prediction System 🚀

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📖 Overview

The Wind Speed Prediction System is a machine learning-powered platform designed to predict wind speed based on historical atmospheric data. This tool is particularly useful for applications in aerospace, research, and meteorology, providing wind speed forecasts, helping with safety assessments and planning.

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🎯 Features

✅ Predict wind speed for different paramters ✅ Supports input in m/s and km/h
✅ Provides output in both m/s and km/h
✅ Visualizes trends with interactive charts
✅ Displays model performance metrics like Mean Squared Error (MSE) and R² Score
✅ Clean and responsive Streamlit-based web interface

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🛠️ Technology Stack

• Python 3.13+
• Streamlit for interactive web UI
• scikit-learn for machine learning
• Pandas, NumPy for data handling
• Matplotlib, Seaborn for data visualization

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🗂️ Project Structure

Wind-Speed-Prediction-System/
│
├── model/                      # Model training and prediction scripts
|   ├──metrics.txt       
|   ├──wind_speed_model.pkl
|   └──wind_speed_predictor.py             
├── app/                        # Streamlit front-end
|   └──app.py                   
├── README.md                   # Project documentation
└── requirements.txt            # Project dependencies

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/ram-narayan-gupta-02/Wind-Speed-Prediction-System.git
cd Wind-Speed-Prediction-System

2. Install Dependencies

pip install -r requirements.txt

3. Run the Streamlit App

python -m streamlit run app/app.py

The app will open in your browser at http://localhost:8501.

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📊 Model Details

The model utilizes historical atmospheric data with features like:

• Latitude & Longitude
• Altitude, Year, Month, Day

Output: Predicted wind speed for valid inputs values.

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⚡ Future Enhancements

• Incorporate real-time weather API support
• Improve model accuracy with advanced algorithms
• Deploy as a cloud-hosted application

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🙌 Acknowledgments

• NOAA for providing historical atmospheric data
• Scikit-learn & Streamlit community for excellent open-source tools

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📩 Contact

Ram Narayan Gupta
📧 [email protected]
🌐 LinkedIn Profile

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If you find this project useful, don't forget to ⭐ the repository!