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-# Predicting Ice Hockey Goals Using Random Forest and XGBoost
-
 ## Intro
 Our project aims to predict goal outcomes in ice hockey by leveraging over 500,000 event-level entries from the Linhac24-25 dataset. Treating it as a binary classification task, the research focuses on identifying key factors—such as puck position, game state, and player context—that influence the likelihood of scoring. The motivation lies in improving tactical insights, player evaluation, and fan engagement by understanding which in-game situations most frequently lead to goals.