Data Analysis and Visualisation(35 marks) Initial analysis of the data using visualisation techniques within Tableau (use diagrams/graphs to highlight important patterns/findings). Discussion and interpretation of result. Discussion of overall trends and patterns observed. Selection of Data Mining Algorithm (10 marks) Select one data mining algorithm suitable for further analysis of your data. Clearly justify your choice, with reference to the visualisation analysis carried out. Data Pre-processing (10 marks) Identify your input and class variables, if relevant (i.e. which variable are you going to consider for your class variables). Identify and resolve any anomalies in the data (i.e. missing values, outliers etc.). Carry out any appropriate pre-processing/transformations to the data set. Data Mining (25 marks) Use the chosen data mining algorithm for further analysis of your pre-processed data set. Clearly discuss the implementation of the data mining algorithm. Discuss and interpret the results. Data Ethics (10 marks) A discussion of data ethical issues related to the analysis and use of business data. Conclusion (10 marks) A discussion of the overall visualisation results (e.g. What were the important findings? Summary of overall trends and patterns). A discussion of the data mining results (e.g. How well did the model fit your data?). A discussion of the business intelligence that can be obtained from these results.
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