Abstract:
In recent year the surge in rental prices in Milan has become a focal point of student-led protests, drawing attention to the broader issue of housing affordability. This research aims to make a prediction of rental prices in Milan by exploiting machine learning approaches. The data are extracted by scrapping from immobiliare.it website. Through the analysis of in-depth data on property characteristics, geographic location, and other relevant factors, predictive models were developed that can provide estimates of rental costs. This study provides a detailed analysis of the process of building the different predictive models, subjecting them to a thorough evaluation of its performance by comparing them with each other. The goal of the work is to provide valuable insights for those who are looking for a new residence in Milan.
