Task 1 consists of the following sub tasks:
a) Assignment 2 requires that you research and critically evaluate literature surrounding the problem of effectively assessing loan applications for credit worthiness. Credit worthiness assessment reduces the risks associated with lending by determining which potential loan applications are considered to be good, or alternatively a poor, credit risk and should on that basis be approved or rejected. Good risk management of loan applications can significantly improve the bottom line of financial institutions such as banks, building societies and credit unions. This research will inform your assessment of the credit data set which is provided forAssignment 2 (About 700 words).
b) Conduct an exploratory analysis of the creditdata.csv data set which is provided on the course study desk to identify what you consider to be top five key variables which contribute to determining whether a potential loan applicant is a good credit risk or a bad credit risk. Note
you should also refer to the data dictionary provided with the creditdata.csv file as this document defines each of the variables and their range of values. Discuss each of your five top variables in about 200 words in terms of the results of your exploratory data analysis using
RapidMiner data mining tool and the relevant supporting literature on credit assessment. Your discussion should also include appropriate statistical analysis results including graphs and tables produced from an exploratory data analysis using RapidMiner data mining tool (about
800 words).
Task 2 (Worth 40 marks) consists of the following sub tasks With the following excel file bicycle-sales.xlsx provided on the course study desk use Tableau 8.0 or a pivot table to produce the four following reports with appropriate accompanying graphs and briefly comment on each report/graph in about 125 words in terms of what trends and patterns are apparent in each report.
The bicycle sales.xlsx file contains the following dimensions and information:
1. Region
2. Sub Region
3. Market
4. Customer
5. Business Segment
6. Category
7. Model
8. Colour
9. Sales Date
10. Sales Period
11. List Price
12. Unit Price
13. Order Quantity
14. Sales Amount
a) Create a report and accompanying graph using Tableau 8.0 or a pivot table that lists by sub region, business segment and model for all mountain bikes sold for the years 2002, 2003 and 2004 and comment on key trends and patterns in this report (125 words approx.)
b) Create a report and accompanying graph using Tableau 8.0 or a pivot table that lists by region, sub region, business segment, unit price and list price for all bicycle clothing for the years 2002, 2003 and 2004 and comment on key trends and patterns in this report (125 words
approx.)
c) Create a report and accompanying graph using Tableau 8.0 or a pivot table that lists by region, sub region, business segment and model for all the road bicycles in order of the total sales for years 2002, 2003 and 2004 and comment on key trends and patterns in this report
(125 words approx.)
d) Create a report and accompanying graph using Tableau 8.0 or a pivot table that lists by category, model, order quality and colour for all bicycles for the years 2002, 2003 and 2004 and comment on key trends and patterns in this report (125 words approx).