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.