This master thesis describes the project ended in the development of three ML/DL-based algorithms that perform the tasks of dress product recommendation, dynamic pricing, and counterfeit detection. The KDD process is addressed in all its constitutive phases. The starting point is data collection, which was performed through web scraping and web crawling techniques, applied over the well-known apparel e-commerce platform YOOX, precisely in the section dedicated to woman dresses. After the data was acquired, we performed a preliminary exploration step, regarding missing values, moving then to actual preprocessing transformations. Here, a specific tailored strategy have been devised for each single field, including standardizations, normalizations, encodings of nominal attributes (One-Hot Encoding, binary), tokenization of textual attributes, missing values imputations.