Below is the description of the different files:

File Name	Description
Practice_concept_drifts.ipynb	A Jupyter notebook to practice drift induction & detection
Practice_concept_drifts_solutions.ipynb	Solutions to the previous Jupyter notebook
full_experiment_real_concept_drifts.ipynb	The full experiment containing the induction of real concept drifts with 9 detectors
full_experiment_gradual_concept_drift.ipynb	The full experiment containing the induction of gradual concept drifts with 9 detectors
full_experiment_seasonal_concept_drift.ipynb	The full experiment containing the induction of seasonal concept drifts with 9 detectors
full_experiment_virtual_concept_drifts.ipynb	The full experiment containing the induction of virtual concept drifts with 9 detectors
drift.py	A python file containing all files to induce a concept drift (real and virtual)
DeepChecks_detectors.py	A python file using DeepChecks functions to detect abrupt, gradual and recurrent concept drifts
evidently_ai_detectors.py	A python file using Evidently AI functions to detect abrupt, gradual and recurrent concept drifts
tensorflow_detectors.py	A python file using Tensorflow functions to detect abrupt, gradual and recurrent concept drifts
drift_detector_with_labels.py	The detectors are: EDDM (Early Drift Detection Method), HDDM_W (Hoeffding Drift Detection Method), ADWIN (ADaptive WINdowing)
drift_detector_multivariate_hdddm.py	A python file using Hellinger Distance Based Drift Detection Method functions to detect abrupt, gradual and recurrent concept drifts
drift_detector_multivariate_md3.py	A python file using the Margin Density Drift Detector functions to detect abrupt, gradual and recurrent concept drifts
drift_detector_multivariate_ollindda.py	A python file using OnLIne Drift Detection Algorithm functions to detect abrupt, gradual and recurrent concept drifts