Satvikv/weather-forecast-ml

Weather Forecasting Using Machine Learning

This project predicts daily Temperature (°C) using atmospheric features such as humidity, wind speed, cloud cover, pressure, and rainfall. The model is trained using XGBoost regression with StandardScaler preprocessing and deployed as an interactive Streamlit application.

Features

• —Predicts temperature using five key weather parameters.
• —XGBoost regression model saved as model.json.
• —Scaler saved as scaler.pkl for preprocessing.
• —Fully containerized using Docker.
• —Deployed on HuggingFace Spaces using the Docker SDK.

Project Files

• —app.py — Streamlit application.
• —model.json — Trained XGBoost model.
• —scaler.pkl — StandardScaler for transforming inputs.
• —requirements.txt — Python dependencies.
• —Dockerfile — Deployment configuration.
• —weather_forecast_data.csv — Dataset used for training.

Run Locally

Install dependencies:

pip install -r requirements.txt

Run the application:

streamlit run app.py

Deployment on HuggingFace (Docker SDK)

1. 1.Create a Space using the Docker SDK.
2. 2.Upload all project files.
3. 3.Click Factory Rebuild from the Settings tab.

Author

Satvik V Artificial Intelligence & Machine Learning