Built a university Pattern Recognition project that compares classical and machine-learning classifiers through feature selection, PCA/LDA transformations, hyperparameter tuning, and cross-validated evaluation on a real binary classification dataset.

The project focuses on analyzing neural activity data to classify neuron types (spiny and aspiny). It integrates unsupervised learning methods (PCA, Autoencoders) and supervised learning models (Logistic Regression, MLP) to build accurate classifiers that effectively analyze neurons' electrical responses.