/IronDiamondtoChallenger

League of Legends Game Data Analysis (Random Forest, KNN, SVM, XGB / Kaggle game data 2017)

Primary LanguageJupyter Notebook

From Iron, Diamond to Challenger

Analyzing game pattern in League of Legends with Machine Learning

  • Based on League of Legends Ranked Games (Mitchell J / Kaggle 2017)
  • EDA : bar, box, pie charts, correlation plots, additional data (champion info) into the original dataset
  • Feature Engineering : created (tags) & dropped (bans, gameid, etc.) different features
  • ML methods : Random Forest, KNN, SVM, GNB and XGB with hyperparameter tuning (ROC/AUC, precision, recall, f1, accuracy metric)
  • Interpretation : PDP isolate plots, shap values and summary plot

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