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This is a robust machine learning pipeline designed to classify Kepler Objects of Interest (KOIs). It determines whether a detected signal represents a real exoplanet or a false positive. The model utilizes a Stacking Ensemble architecture, combining the predictions of three powerful gradient boosting frameworks (LightGBM, XGBoost, and CatBoost) and aggregating them using a final LightGBM meta-learner. It is specifically engineered to handle missing data (NaNs) in scientific datasets through a dual-strategy imputation pipeline.