This project implements a Bayesian network model for weather prediction using artificial intelligence (AI) techniques. The Bayesian network captures complex relationships among weather variables such as precipitation, temperature, and wind, allowing for probabilistic inference and prediction of weather conditions.
- Utilizes Bayesian network principles for uncertainty modeling in weather prediction.
- Implements data preprocessing techniques including cleaning, discretization, and numerical labeling.
- Develops a scalable and efficient Bayesian network model for weather prediction.
- Evaluates model performance using accuracy metrics on testing datasets.
- Provides insights into probabilistic relationships among weather variables.
- Clone the repository.
- Install the necessary dependencies from requirements.txt
- Run the provided by navigating to UI_Interface and run the python file.
- Analyze the results and insights gained from the Bayesian network model.
- Python 3.x
- NetworkX
- Matplotlib
- Pandas
- NumPy
- tkinter