Use APIs like OpenWeatherMap or scrape meteorological websites with Python to gather historical and real-time weather data.
Clean and format the dataset using libraries like Pandas, handling missing values, duplicates, and converting timestamps for analysis.
Extract meaningful features such as temperature trends, humidity levels, and seasonal patterns to enhance prediction accuracy.
Implement models like linear regression or decision trees with Scikit-learn to predict weather conditions based on input data.
Export predictions to Tableau to create dynamic dashboards with graphs, heatmaps, and charts for visual analysis of trends.
Automate data updates and refine the model with additional data. Share Tableau dashboards to provide accessible insights.