Ronen Huang
May 2022 to June 2022 (initial). December 2024 (improvement).
The individual FIFA data from 2015 to 2022 can be accessed via https://www.kaggle.com/datasets/stefanoleone992/fifa-22-complete-player-dataset.
To preprocess the FIFA data for usability on machine learning tasks and predict injury proneness.
from pipeline import load_clean_train
load_clean_train()The combined and cleaned data is saved in the data directory as players_15_to_22_data.csv.
The training of the Random Forest classifier with hyperparameter tuning on the number of trees, maximum depth, and maximum features is saved in the tables directory as random_forest_train_table.csv and the scatter plot is saved in the plots directory as random_forest_pr_auc_plot.jpg.
The test predictions and labels of the best Random Forest classifier is saved in the tables directory as random_forest_test_predictions.csv and the confusion matrix of the test performance is saved in the plots directory as random_forest_confusion_matrix_plot.jpg.
The training of the LightGBM classifier with hyperparameter tuning on the learning rate, number of leaves, number of trees, and injury prone label weight is saved in the tables directory as lightgbm_train_table.csv and the scatter plot is saved in the plots directory as lightgbm_pr_auc_plot.jpg.
The test predictions and labels of the best LightGBM classifier is saved in the tables directory as lightgbm_test_predictions.csv and the confusion matrix of the test performance is saved in the plots directory as lightgbm_confusion_matrix_plot.jpg.
The test metrics of the best Random Forest classifier, best LightGBM classifier, Logistic Regression, and Baseline are saved in the tables directory as test_metrics.csv and the Precision Recall curve on the test data is saved in the plots directory as precision_recall.jpg.
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., . . . Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 3149–3157). Curran Associates Inc.