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This repository contains the dataset and code used in the manuscript "An Automated Machine Learning Approach for Near-Real-Time Rockfall Classification via Distributed Acoustic Sensing Along Linear Infrastructure." File Descriptions: Data_set/: Contains feature files used for training, validating, and testing the classifier. Figures/: Includes the figures generated by the provided code. Models/: Stores the trained classification model files. best_params_6s.pkl: A file containing the optimized hyperparameters for the Random Forest classifier. RF_Classifier_four_time_windows.ipynb: Jupyter notebook to compare the effect of four different time window lengths on classification accuracy. RF_Classifier_6s_impacts_features.ipynb: Jupyter notebook to analyze the impact of various features on classification accuracy. RF_Classifier_6s_windows_ParaTun.ipynb: Jupyter notebook for parameter optimization of the classifier. Model_test.ipynb: Jupyter notebook to test the performance of the classifier.