Development of a Regression Model to Predict Global Warming with Machine Learning

Global warming is a phenomenon caused by the increase of greenhouse gases, affecting the global climate, ecosystems, and human health. The alteration of climate patterns and the occurrence of extreme phenomena affect the natural habitats of various species, causing forced migrations, population reduction, and extinction of species. This research uses a simple linear regression (SLR) model based on Machine Learning (ML) to predict the global average temperature (°C) in the short, medium, and long term. Based on historical data and temporal forecasting techniques, the model allows for forecasting future scenarios and assessing possible environmental risks. The developed SLR model performed well (R²=0.7383), the results underline the importance of accurate predictions for creating effective climate change mitigation policies and strategies.