Learning Outcomes tested

Learning Outcomes tested
(from module syllabus)

1. Display a detailed knowledge and systematic understanding of the current trends in data warehousing, business intelligence and data mining.
2. Demonstrate a comprehensive knowledge and systematic understanding of essential concepts, principles and tools to build, test and debug a data warehousing and data mining application that provide business intelligence to its users.
3. Demonstrate a comprehensive knowledge and systematic understanding of essential concepts, principles and tools to build, test and debug a data warehousing and data mining application that provide business intelligence to its users.
TASK DESCRIPTION
Analyse the Statlog (German Credit Data) data set (available from the UCI Machine Learning Repository – https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29) to explore the different factors that affect the credit rating of a person. Your aim is to identify which attribute or combination of attributes and which algorithm has the highest accuracy in identifying adults who would be given a ‘good’ credit rating. Once you have completed this, write a report to describe in detail the analyses you have performed.

Your report should include:
• A data set description in terms of the attributes present in the data, the number of instances, missing values, and other relevant characteristics.
• A detailed description of the pre-processing of the data.
• Evidence that you have investigated the data using multiple analysis methods.
• An explanation of the selected algorithm.
• A discussion of any pre or post processing done to improve the accuracy of your analysis.
• A business recommendation based upon your analysis.
The report should be no more than 2500 words long and should include such graphics as are appropriate to illustrate your answers.

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