Versatile Machine Learning Classification Tool for Power Quality Disturbances

The PQD ML Classifier App is a robust tool designed to facilitate the classification of power quality disturbances (PQDs) using machine learning algorithms. Users can import datasets and labels generated by the previous apps, then select from various classifiers, including k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gaussian Naive Bayes (GNB). The app allows customization of the train-test split ratio, k-fold cross-validation, and hyperparameter optimization through Grid Search or Random Search. Once the model is trained, users can test its performance and obtain metrics such as accuracy, precision, recall, and F1 score. This app provides a streamlined, no-code approach to implementing and evaluating machine learning models for PQD classification.