Publicação
Beyond Intentions: A Data-Driven Model of Brazilian Migration Destinations for Decision-Making: Predicting individual migration trends to support strategic decisions
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Rodrigues, Teresa Maria Ferreira | |
| dc.contributor.advisor | Santos, Augusto José Rabelo Almeida | |
| dc.contributor.author | Fenner, Priscila Dias | |
| dc.date.accessioned | 2025-11-11T10:52:08Z | |
| dc.date.available | 2025-11-11T10:52:08Z | |
| dc.date.issued | 2025-10-28 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Marketing Intelligence | pt_PT |
| dc.description.abstract | This thesis explores the motivations and challenges faced by Brazilian emigrants, aiming to bridge the gap between descriptive migration studies and predictive modelling. While existing research has predominantly focused on macro-level flows or qualitative analyses, this study adopts a novel approach by building and analysing an original dataset of 173 survey responses from Brazilian migrants to Australia and Ireland. To uncover the patterns influencing destination choices and migration experiences — and to move beyond explanation toward prediction — this study applies a supervised machine learning model (Random Forest), preceded by Principal Component Analysis (PCA) for dimensionality reduction and feature selection. Despite limitations such as a modest sample size, class imbalance, and the presence of noisy self-reported data, the model achieves an overall accuracy of 73% in predicting destination country, correctly identifying 81% of respondents who migrated to Australia and 60% to Ireland. The most influential features are predominantly qualitative, including perceived cost of living, use of social media and messaging apps to access information, and motivations such as language acquisition and job opportunities. These results suggest that behavioural traits and perceptions outweigh sociodemographic characteristics in determining migration outcomes. The findings offer technical insights for institutions such as universities, immigration agencies, and private service providers targeting migrants. The model enables the identification of undecided or influenceable migrant profiles, supports the development of communication strategies tailored to digital information-seeking behaviours, and highlights optimal moments for outreach. Additionally, by introducing a reproducible pipeline for predictive modelling based on primary behavioural data, the study addresses a key methodological gap in current migration research. By shifting the analytical lens from aggregate flows to individual-level decisions, it demonstrates that predictive analytics can generate meaningful insights even within small and complex datasets. This approach contributes a new perspective to migration research and offers institutions a scalable tool to plan strategically and respond proactively to emerging migration trends based on data, rather than assumptions. | pt_PT |
| dc.identifier.tid | 204070597 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190468 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Brazilian Migration | pt_PT |
| dc.subject | Predictive Modelling | pt_PT |
| dc.subject | PCA | pt_PT |
| dc.subject | Random Forest | pt_PT |
| dc.subject | Migration Prediction | pt_PT |
| dc.subject | SDG 4 - Quality education | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.subject | SDG 10 - Reduced inequalities | pt_PT |
| dc.title | Beyond Intentions: A Data-Driven Model of Brazilian Migration Destinations for Decision-Making: Predicting individual migration trends to support strategic decisions | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Marketing Analítico, especialização em Marketing Intelligence | pt_PT |