Description

Weather forecasting impacts agriculture, transportation, and disaster management. This research applies machine learning techniques to predict weather patterns using key variables like precipitation, temperatures, and wind speed.

Methodology

• Dataset and Variables: Utilizes preprocessed weather data.
• Machine Learning Models: Decision Tree, Random Forest, and XGBoost.
• Evaluation Metrics: Measures performance with MAE, MSE, and R-squared.

Results

• Model Insights: Analyzes model effectiveness in weather prediction.
• Visualizations: Heatmaps and comparison plots aid in result interpretation.

Discussion

• Model Performance: Examines strengths and limitations of each model.
• Comparative Analysis: Compares model effectiveness based on metrics.
• Challenges and Future Directions: Discusses challenges faced and future research possibilities.