Métodos Preditivos de Data Mining


The curricular unit of data mining primary goal is to provide students with a fundamental understanding of Predictive Analytics as it relates to improving business performance.

This course will cover the basics of predictive analytics and modelling data to determine which algorithms to use and to understand the similarities and differences and which options affect the models most.

Topics covered include predictive analytics algorithms for supervised learning, including decision trees, neural networks, k-nearest neighbour, support vector machines, and model ensembles.

At the end of the course, participants will be able to use these skills to produce a fully processed data set compatible with building powerful predictive models that can be deployed to increase profitability.

Caracterização geral





Professor responsável

Roberto André Pereira Henriques


Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês




Método de ensino

The course is based on a mix of theoretical lectures and practical lectures and tutorials. The theoretical sessions include the presentation of theoretical concepts and methodologies as well as application examples.

The main objective of the practical classes is to familiarize students with the software to perform the analysis and data explorations tasks.

Método de avaliação

Regular examination period (1st examination period)

  • Exam (60%)
  • Project (40%)

Resit examination period (2nd examination period)

  • Exam (60%)
  • Project (40%)

Minimum grade of 8.0 (in 20) for the exam
Minimum grade of 4.0 (in 20) for the project


The course is divided in the following learning units (LU):

LU1. Introduction to predictive modeling

LU2. Data pre-processing

LU3. Introduction to classifiers

  • Linear and Logistic regression
  • Bayesian learning systems
  • Instance-based learning and classification
  • Regression and classification trees
  • Ensemble classifiers CUC4. Predictive models evaluation
  • Neural networks and Deep Learning Neural Networks

LU4. Predictive models evaluation

LU5. Develop a projet using SAS EMiner