Assignment 1
The aims of this assignment are to put into practice the concepts covered in lectures and apply these to a real dataset of your own choosing, and to demonstrate your ability to use R to carry out data analytics tasks.
The main steps you need to carry out and report on are:-
Selecting a dataset suitable for analysis and identifying the problem you wish to explore.
Summarising and visualising your data set.
Preparing your dataset for analysis (data cleansing, choosing appropriate features to model) etc..
Choosing an appropriate model or models for your chosen problem and building, running and evaluating these.
Presenting and interpreting the results of your models and relating these to your initial problem.
The choice of data and problem to work on are entirely up to you. There are lots of public data sets available (with some suggestions at the end of this page) and you should check with me first that the data you have chosen is appropriate. The emphasis is also very much on the process: if you find that the techniques you have chosen don’t work very well or fail to produce particularly interesting results, then this is not a problem provided you followed the appropriate steps to understand and prepare the data and select appropriate models.
Your assignment needs to be presented as a 10-page (maximum) report in PDF format, and you should also submit the R code used and developed as an appendix to this report.
This assignment is worth 50% of the marks for the class and will follow the following marking scheme:
Selecting a dataset suitable for analysis and identifying the problem you wish to explore. (5%)
Summarising and visualising your data set. (10%)
Preparing your dataset for analysis (data cleansing, choice of features) etc.. (10%)
Choice of model, evaluation and validation (15%)
Interpretation and explanation of the results of your models and implications of these for your initial problem. (10%)
Your report should be submitted by midnight on Sunday 26th February 2017.
Some hints on choosing data sets
There are huge numbers of public data sets available and here are just a few suggestions:-
http://archive.ics.uci.edu/ml/ currently has 360 data sets maintained for the machine learning community, many of which have been studied and analysed and used in research papers
http://finance.yahoo.com/market-overview/?bypass=true is a huge source of financial data (if that’s a route that appeals)
https://data.glasgow.gov.uk/ various open data sets (of varying quality) relating to Glasgow. Other cities offer similar services
Just search for something that you are interested in
No need to make things too hard for yourselves but at the same time try and avoid data sets that have already been extensively studied.
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