data_preparation/README.md

data_preparation/

Load, clean, split .npz data for training/notebooks. Important: recompute head_deviation from clipped yaw/pitch (see prepare_dataset.py). 10 features for face_orientation: head_deviation, s_face, s_eye, h_gaze, pitch, ear_left, ear_avg, ear_right, gaze_offset, perclos.

prepare_dataset.py: load_all_pooled(), load_per_person() for LOPO, get_numpy_splits() (XGBoost), get_dataloaders() (MLP). Cleans yaw/pitch/roll and EAR to fixed ranges. Face_orientation uses 10 features: head_deviation, s_face, s_eye, h_gaze, pitch, ear_left, ear_avg, ear_right, gaze_offset, perclos.

data_exploration.ipynb: EDA — stats, class balance, histograms, correlations.

Import from models.mlp.train / models.xgboost.train / notebooks — don’t run this module standalone.