Weather Predictive System By Using Machine Learning

Abstract

Forecasting the weather has a significant impact on both life and production. Technological advancements have led to the emergence of many weather forecasting techniques, including numerical weather forecasting, quantitative forecasting, and weather map forecasting. These conventional techniques for analysing data, however, have drawbacks, including inadequate objectivity, limited analysis, and an inability to make accurate weather predictions. The purpose of this predictive system is to forecast the weather and alert individuals to changes in air quality or other weather conditions that might have an impact on their general health. It is still difficult to provide a prediction model for climate forecasting that is both highly accurate and effective. Thus, by combining machine learning with this prediction method, precise results may be achieved. This prediction allows humans to get warnings in advance of natural calamities such as floods. This will lessen property damage and assist preserve the lives of residents in low-lying regions. This technique primarily aids farmers in preventing serious crop damage. Therefore, when compared to Naive Bayes (NB), this suggested model Support Vector Machine (SVM) does better when it comes to F1 Score, Accuracy, and Recall.