This project aims to predict weather conditions based on past data points. It is a comprehensive exploration of Bayesian Networks and their application in weather forecasting. The team behind this project includes:
- EL YOUSFI-ALAOUI Mohammed
- El Ajjouri Safaa
- Tlemcani Chayma
- Motassim Ahmed Taha
The notebook delves into the intricacies of modeling weather patterns using Bayesian methods, providing a step-by-step guide to understanding and implementing these networks for predictive purposes.
- Data Preprocessing: Insights into how the data is cleaned and prepared for modeling.
- Model Training: Detailed explanation of the Bayesian Network model, its setup, and training process.
- Accuracy Measurement: Methods used to evaluate the model's performance and accuracy in predicting weather conditions.
- Predictive Analysis: A demonstration of the system's capability to forecast weather based on historical data.
The notebook showcases the effectiveness of Bayesian Networks in weather prediction through a series of analyses and evaluations. Key sections include:
- The use of the
DataFrame()constructor for data manipulation. - The application of the
inplaceargument for direct modifications on the original dataframe. - Strategies for calculating model accuracy and storing predictions (
y_pred). - The division of data into training and test sets for robust model evaluation.
The Weather Prediction System using Bayesian Networks exemplifies the power of machine learning in environmental science. Through meticulous data preparation, model training, and evaluation, this project highlights the potential of Bayesian Networks in forecasting weather conditions with notable accuracy.