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 # Predicting Ice Hockey Goals Using Random Forest and XGBoost
 
 ## Intro & Background
-
-This project explores the prediction of goals in professional ice hockey games using machine learning techniques.  
-The main goal is to determine which features most effectively predict goal-scoring events, with a particular focus on shot attempts.  
-Predicting goals is valuable for teams, analysts, and broadcasters to better understand game dynamics.  
-While traditional stats like shot counts have been used, machine learning offers deeper, data-driven insights.
-
-Ice hockey is a fast-paced sport with frequent possession changes and rapid movement, making goal prediction challenging.  
-The dataset contains in-game event data including player positions, shot locations, man-power situations, and contextual game details.  
-Understanding basic ice hockey rules and terms like "manpower situation" or "xG" (expected goals) will help interpret the models.
+This project explores predicting goals in professional ice hockey using machine learning, focusing on identifying features—especially shot 
+attempts—that most effectively forecast goal-scoring events. Accurate goal prediction is valuable for teams, analysts, and broadcasters aiming 
+to better understand game dynamics. While traditional stats like shot counts provide some insight, machine learning enables deeper, data-driven analysis. 
+The fast-paced nature of ice hockey, with constant possession changes and rapid movement, makes prediction complex. The dataset includes in-game events such as player positions, 
+shot locations, manpower situations, and other contextual game details. Familiarity with hockey concepts like "manpower situation" and "xG" (expected goals) is helpful for interpreting model outcomes.
 
 ## Algorithms