Title
Machine and Deep Learning models for house price prediction in United States of America and Portugal
Author
Sanchez de La Fuente, Catarina de Freitas
Summary
pt
A presente estudo utilizou a medotologia CRISP-DM para a caraterização e descrição
do processo de desenvolvimento de um sistema para estimação dos preço das casas em
Portugal. Duas fases importantes no processo foram: a extração de dados e a comparação
entre vários algoritmos. A extração de dados foi realizada através de técnicas de Web
Scraping a partir do site Mais Consultores [1]. Utilizaram-se métodos de Text Mining
- Rule-based Matching e Similarity – para estruturar e retirar significado da informação
que se extraiu do site. De seguida, realizámos a comparação entre a aplicação de algoritmos
de Machine Learning e Deep Learning: 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.
Encontrar esta solução constituiu a principal motivação da presente tese. Os resultados
obtidos pelos algoritmos utilizados, tanto os de Machine Learning como os de
Deep Learning, demonstram que os algoritmos precisavam de mais dados para treino.
Adicionalmente, os algoritmos com melhores resultados, i.e., com menor Mean Absolute
Error (MAE), Mean Square Error (MSE) e Root Mean Square Error (RSME) e maior
score foram os algorimos de Deep Learning.
en
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.