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

  • Simona Nobile (Student)

Student thesis: Master's Thesis

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 Award4 Jul 2024
Original languageEnglish
Awarding Institution
  • Universidade Católica Portuguesa
SupervisorPedro Afonso Fernandes (Supervisor)

UN SDGs

This student thesis contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • Machine learning
  • House pricing prediction
  • Panel data

Designation

  • Mestrado em Análise de Dados para Gestão

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