Your task is to build a model that predicts whether an exoplanet is potentially habitable (1) or non-habitable (0) based on 377 features (see metaData.csv).

Due to the imbalanced nature of the dataset—where habitable planets are much rarer than uninhabitable ones—the evaluation metric is the F1-score, which balances precision (the proportion of correct positive predictions) and recall (the proportion of actual positives identified). This is more informative than accuracy alone; for example, a model that always predicts the majority class could achieve high accuracy but would fail to detect habitable planets.

You are encouraged to experiment with oversampling techniques (e.g., SMOTE), class weighting, or other methods to address the imbalance and improve detection of the minority class.