/me315-project-tzvetanmoev

me315-project-tzvetanmoev created by GitHub Classroom

ME315: Assessed Coursework - Project

  • You will undertake a project that will determine your final mark of the course by 50 per cent. The project will require you to analyse one or more real world datasets of your choice. You can use the UCI repository or any other accessible dataset.

  • The project will consist of two tasks than can be chosen from the following

    1. Regression: where the problem consists of continuous target variable(s).
    2. Classification: where problem consists of categorical target variable(s).
    3. Unsupervised Learning, i.e. Factor/Cluster Analysis: where the problem consists of identifying homogeneous population groups or variables.

  • The tasks may be applied on a single dataset or two different ones.

  • You will be expected to present the empirical problem, consider and implement competing methods to use the available data to address it. The list methods should definitely include some of the techniques covered in the course. You may include other methods; this is entirely up to you.

  • In all cases, the output from these techniques should be described in non-technical language targeting people with a minimal quantitative background.

  • 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 soft on Moodle, your R code should also be submitted as a R markdown file via GitHub Classroom.

  • The project is due Friday, July 5th at 10 am.