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Publications

2019

A Data Mining Approach for Predicting Academic Success – A Case Study

Authors
Martins, MPG; Miguéis, VL; Fonseca, DSB; Alves, A;

Publication
Advances in Intelligent Systems and Computing

Abstract
The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure. © 2019, Springer Nature Switzerland AG.

2019

Evaluating the short-term effect of cross-market discounts in purchases using neural networks: A case in retail sector

Authors
Migueis, VL; Camanho, AS; Falcao e Cunha, JFE;

Publication
Expert Systems

Abstract
Promotional tools such as cross-market discounts have been increasingly used as a means to increase customer satisfaction and sales. This paper aims to assess whether the implementation of a cross-market discount campaign by a retailing company encouraged customers to increase their purchases level. It contributes to the literature by using neural networks to detect novelties in a real context involving cross-market discounts. Besides the computation of point predictions, the methodology proposed involves the estimation of neural networks prediction intervals. Sales predictions are compared with the observed values in order to detect significant changes in customers' spending. The use of neural networks is validated through the comparison with the forecasting estimates of support vector regression, regression trees, and linear regression. The results reveal that the promotional campaign under analysis did not significantly impact the sales of the rewarded customers. © 2019 John Wiley & Sons, Ltd.

2018

Exploring the use of deep neural networks for sales forecasting in fashion retail

Authors
Loureiro, ALD; Migueis, VL; da Silva, LFM;

Publication
DECISION SUPPORT SYSTEMS

Abstract
In the increasingly competitive fashion retail industry, companies are constantly adopting strategies focused on adjusting the products characteristics to closely satisfy customers' requirements and preferences. Although the lifecycles of fashion products are very short, the definition of inventory and purchasing strategies can be supported by the large amounts of historical data which are collected and stored in companies' databases. This study explores the use of a deep learning approach to forecast sales in fashion industry, predicting the sales of new individual products in future seasons. This study aims to support a fashion retail company in its purchasing operations and consequently the dataset under analysis is a real dataset provided by this company. The models were developed considering a wide and diverse set of variables, namely products' physical characteristics and the opinion of domain experts. Furthermore, this study compares the sales predictions obtained with the deep learning approach with those obtained with a set of shallow techniques, i.e. Decision Trees, Random Forest, Support Vector Regression, Artificial Neural Networks and Linear Regression. The model employing deep learning was found to have good performance to predict sales in fashion retail market, however for part of the evaluation metrics considered, it does not perform significantly better than some of the shallow techniques, namely Random Forest.

2018

A Data Mining Approach to Predict Undergraduate Students' Performance

Authors
Martins, MPG; Migueis, VL; Fonseca, DSB;

Publication
2018 13TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI)

Abstract
This paper presents a methodology based on random forest algorithm to predict the undergraduate academic performance of students from a polytechnic institution. The approach followed enabled to select 11 explanatory variables, starting from an initial set of around fifty, which allow to obtain a good predictive performance (R-2=0.79). These variables reveal crucial aspects for the definition of management strategies focused on promoting academic success.

2018

Educational Data Mining: A Literature Review

Authors
Martins, MPG; Migueis, VL; Fonseca, DSB;

Publication
2018 13TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI)

Abstract
With the aim of disseminating the potential and the capacity of Educational Data Mining (EDM) as an instrument of investigation and analysis in the support to the management of Higher Education Institutions, this paper presents a brief description of some of the most relevant studies in the area. The analysis carried out allows to highlight the innovations that EDM has been promoting, as well as current and future research trends.

Supervised
thesis

2019

Returns in the luxury fashion e-commerce: predicting and understanding their impact in the customer experience

Author
José João Guimarães Fernandes

Institution
UP-FEUP

2019

Use of data mining techniques to support taxi vehicles operations

Author
Ana Luísa Correia Dias Loureiro

Institution
UP-FEUP

2019

Exploring fish purchasing behaviour using data analytics

Author
Rodrigo Teodoro Passos

Institution
UP-FEUP

2019

Churn Prediction in Digital Marketing

Author
Inês de Carvalho Pereira Ferreira

Institution
UP-FEUP

2019

Learning to Classify a Subject-Line Quality for Email Marketing Using Data Mining Techniques

Author
Maria João dos Santos Aguiar e Mira Paulo

Institution
UP-FEUP