Climate Change: Modeling Earth Surface Temperatures
Date:
This presentation explored a supervised machine learning regression project forecasting global average temperatures using historical meteorological data spanning over a century. Without relying on existing weather forecasts, the project leverages historical global temperature averages (land, ocean, maximum, and minimum) to build and evaluate multiple regression models including Linear Regression, KNN, Random Forest, SVM, and Gradient Boosting, culminating in an analysis of the best performing model with partial dependence plots. Notebook.