Comparative Analysis of ML Models for Climate Change Prediction with GridSearchCV Optimization

An evaluation study is assessed for the various efficient machine learning models such as Random Forest, Gradient Boosting, and Neural Networks, for climate-related prediction information using the dataset "Climate Insights" from Kaggle. Key variables like temperature, CO2 emissions, sea level rise, precipitation, humidity, and wind speed are all included in the dataset. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the performance of the model. Gradient Boosting has the best prediction accuracy among the models, as evidenced by its lowest error rates. All models show under fitting at average values and a limited ability to capture extreme variations, even with parameter adjustment. The results highlight the potential of machine learning techniques in climate data predictions as well as their present difficulties.