You will undertake a project that will determine your final mark of the course by 30 percent. The project will require you to analyze one or more real-world
datasets that will become available on the Moodle page of the course. The analysis will consist of three parts:

1. In the first part you will be given data collected from various individuals on several variables. The goal will be to use unsupervised learning techniques such as Principal Component Analysis or Cluster Analysis to summarise the information in the data by appropriate tables and/or plots.

2. In the second part you will be presented with a regression problem. The aim would be to compare various models and techniques for their estimation to allow meaningful interpretation and competitive predictive performance. The latter should be assessed by appropriate experiments based on training and test datasets. In addition to linear regression, Tree-based methods, Non-linear models, or other suitable techniques can be used if you think they can provide improvement.

3. Finally, in the third part you will be given a classification problem. The analysis will contain similar steps to the second part but you should be able to interpret the output from different models and compare their predictive performance taking into account that the response variable will be binary. In addition to appropriate regression or discriminant analysis, Tree-based methods, Non-linear models, or other suitable techniques can be used if you think they will perform better.