The coursework is an individual assessment weighted 50% of the marks for the module. It is primarily an exercise in applying data mining knowledge and techniques to a practical problem.
data mining
Description
CC7164 Data Mining for
Business Intelligence
Spring Semester 2018-2019
Coursework Assignment
The coursework is an individual
assessment weighted 50% of the marks for the module. It is primarily an
exercise in applying data mining knowledge and techniques to a practical
problem. This assignment involves the analysis one of the UK's largest
collection of UK and international social, economic and population data
provided by government departments and agencies, public bodies and local
authorities. You are asked to mine the data to discover interesting patterns of
UK and provide appropriate documentation detailing the stages of the CRISP-DM
method and a critical evaluation of possible benefits and commercial risks of the
project.
Coursework
Submission
The final report
of your mining project is due on Friday, 10th May 2019. All your work must be documented into a single PDF file. It should have a cover page with module code and title,
your data mining project title, student ID and name. The document must be submitted via WebLearn.
Please note that plagiarism is a serious academic
offence, for which penalties are severe. All suspected cases of plagiarism will
be reported.
Detailed Specifications
The dataset used for
the coursework is based on real-world data sets provided by data published by
government departments and agencies, public bodies and local authorities, which
are available at https://www.ukdataservice.ac.uk/manage-data/lifecycle,
https://data.gov.uk/
and https://data.london.gov.uk/ You
are asked to use SAS Enterprise Miner to mine the data to discover some
interesting patterns of UK such as health, education, population, business,
sports, lifestyle, crime, wealth, property, etc.
Your project proposal
with chosen mining topics and data sets will be peer reviewed and have to be
approved by the module leader by week 4 via Discussions on the module weblearn.
The final report
should include details of the following stages of the CRISP-DM method and a critical
evaluation of possible benefits and commercial risks of the project.
·
Business
and data understanding
·
Data
preparation and exploration
·
Modelling
·
Evaluation
and results explanation
·
Plan
for deployment
·
Evaluation
of possible benefits and commercial risks
