House price prediction

: a comparative analysis of machine learning approaches to study Melbourne’s market

Abstract

This thesis work investigates the application of machine learning (ML) techniques for predicting house prices, a crucial task with widespread implications. In this scope, this work presents a literature review on state-of-the-art approaches and a practical experiment using a dataset of house sales in Melbourne, Australia. The analysis focuses on identifying key features for price prediction and assessing the performance of various ML algorithms. In fact, examining feature importance over time, it is possible to understand the dynamic nature of house price prediction.

Date of Award	4 Jul 2024
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Pedro Afonso Fernandes (Supervisor)