Fashion Item Classification

This project focuses on classifying fashion items using Machine Learning techniques applied to the Fashion MNIST dataset.

Two different learning approaches were implemented and compared:

Logistic Regression (Supervised Learning)

K-Means Clustering (Unsupervised Learning)

The dataset was reduced to 5 fashion categories to simplify classification and improve interpretability.