The project will consist of completing the following three tasks that can be implemented on one real-world dataset.

1. Unsupervised Learning: where the problem consists of identifying homogeneous population groups or dimension reduction techniques, which can then be used in the context of the empirical application
2. Regression: where the problem consists of continuous target variable(s).
3. Classification: where problem consists of categorical target variable(s).

You will be expected to present each of the datasets you are analysing, identify research questions that can be addressed by your analysis and, ideally, present relevant existing literature and contrast your results against it. You are expected to use multiple technique for the regression and classification tasks, and compare their results.

In all cases, your analysis should be presented in a paper like format, avoiding highly technical language where possible. It may be helpful to think of your audience as consisting of people with some quantitative background but no prior knowledge of Machine Learning. Your ability to present and interpret the results will be regarded as important as your ability to apply the taught techniques.

The results of the project should be presented in a 10-page article in A4 format. The 10-page limit includes figures and tables but excludes the title page, table of contents and references. In addition to the 10-page article, which should be submitted via a word file, your R code should also be submitted with appropriate comments and description via an R script or an RMarkdown file.

Just provide the open access links in your code files, from which the data can be downloaded.

To sum up the following two files are required:

1. A word or pdf file with your report that should not contain any code (10-page limit applies as mentioned above).
2. Your code in a single file of appropriate format (R script, RMarkdown)