Description

In this notebook, I analyze global temperature change data to predict whether a country's average temperature rise will exceed the critical +1.5°C threshold. The data was cleaned, reshaped, and standardized to prepare it for modeling. A Support Vector Machine (SVM) with an RBF kernel was used for classification, and hyperparameter tuning was performed using grid search to optimize performance. The final model achieved pretty strong accuracy and demonstrated improvements in detecting countries exceeding the threshold. Visualizations, including a world map and classification metrics,