1. demonstrate applied knowledge of people, markets, finances, technology and management in a global context of business intelligence practice (data warehouse design, data mining process, data visualisation and performance management) and resulting organisational change and how these apply to implementation of business intelligence in organisation systems and business processes 
2. identify and solve complex organisational problems creatively and practically through the use of business intelligence and critically reflect on how evidence based decision making and sustainable business performance management can effectively addressing real world problems 

4. demonstrate the ability to communicate effectively in a clear and concise manner in written report style for senior management with correct and appropriate acknowledgment of main ideas presented and discussed. The key frameworks, concepts and activities covered in modules 2-12 and more specifically modules 6 to 12 are particularly relevant for this assignment. This assignment consists of three tasks 1, 2 and 3 and builds on the research and analysis you conducted in Assignment 2. Task 1 is concerned with developing and evaluating a model of key factors impacting on credit risk ratings for loan applications in determining whether approve a loan or not approve a loan. Task 2 is concerned with the key opportunities and challenges associated with the implementation and utilisation of business intelligence systems. Task 3 is concerned with performance management and provides you with the opportunity to design and build a sales performance dashboard using pivot tables and Tableau 7.0 Desktop. 

Task 1 
In Task 1 of this Assignment 4 you are required to follow the six step CRISP DM process and make use of the data mining tool RapidMiner to analyse and report on the creditrisk_train. csv and creditrisk_score.csv data sets provided for Assignment 4. You should refer to the data dictionary for creditrisk_train.csv (see Table 1 below). In Task 1 and 2 of Assignment 4 you are required to consider all of the business understanding, data understanding, data preparation, modelling, evaluation and deployment phases of the CRISP DM process. 

a) Research the concepts of credit risk and credit scoring in determining whether a financial institution should lend at an appropriate level of risk or not lend to a loan application. This will provide you with a business understanding of the dataset you will be analysing 
in Assignment 4. Identify which (variables) attributes can be omitted from your credit risk data mining model and why. Comment on your findings in relation to determining the credit risk of loan applicants. 
b) Conduct an exploratory analysis of the creditrisk_train.csv data set. Are there any missing values, variables with unusal patterns? How consistent are the characteristics of the creditrisk_train.csv and creditrisk_score.csv datasets? Are there any interesting 
relationships between the potential predictor variables and your target variable credit risk? (Hint: identify the variables that will allow you to split the data set into subgroups). Comment on what variables in the data set creditrisk_train.csv might influence