INTEGRATING PREDICTIVE ANALYTICS AND MACHINE LEARNING FOR WEATHER FORECASTING

Abstract

To make accurate predictions about future weather and environmental conditions, predictive analytics makes use of cutting-edge data analysis tools like statistical modeling and machine learning. Predictive models are able to offer precise forecasts of important environmental variables including air quality, humidity, precipitation, and temperature by evaluating massive information collected from sensors, satellites, and weather stations. This study provides a comprehensive examination of findings from weather forecasting utilizing scatter plots, outputs from Ordinary Least Squares (OLS) models, computations of errors, and evaluations of accuracy, with a special emphasis on decision tree models. This methodological framework greatly aids in the progress of machine learning techniques by guaranteeing the creation of trustworthy models that can accurately anticipate future outcomes. The results show that machine learning approaches in weather forecasting have made great strides, leading to more accurate predictions.