Machine and Deep Learning models for house price prediction in United States of America and Portugal

• Autor(a) principal: Sanchez de La Fuente, Catarina de Freitas
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/27005
• topic: House price prediction; Machine learning; Deep learning; Text mining; Preços das casas

The present study describes the development process of a system to predict the houses’ prices in Portugal. Two main phases of this process were the data extraction and the comparison among several algorithms. Data Extraction was made through Web Scraping techniques applied the Mais Consultores site [1]. This study used Text Mining methods - Rule-based Matching and Similarity – in order to structure and obtain meaning from the information extracted. Afterwards, this thesis made a comparison among the application of Machine Learning and Deep Learning algorithms: Support Vector Machines (SVM), Decision Tree Regressor (DTR), Random Forest, K-Nearest Neighbour (KNN), Artificial Neural Networks ANN, Convolutional Neural Networks CNN, Recurrent Neural Networks RNN, Multi-layer Perceptron MLP and Long Short-term Memory LSTM Network. Finding this solution was the prime motivation of the present thesis. The results obtained by the used algorithms, both Machine Learning and Deep Learning, demonstrated that the algorithms needed more data for the training set. Additionally, the algorithms with the best results, i.e., with the lesser value of Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RSME) and the better score were the Deep Learning algorithms.