Description

Coursework 2(Part A and Part B)

Coursework Requirements:

You are required to analyse a large data set of your choice, which has been agreed with your module tutor. The analysis including all results should be submitted in the form of a complete report. 

Your project may use any combination of data analysis techniques, data-mining algorithms and software that has been covered in the module.  You may also apply them to any aspect(s) of the dataset for knowledge discovery. You should cover the areas indicated below and your findings should be presented in the form of a report(include excel sheet) to the managers of your company.  You will also be expected to give an oral presentation to these mangers. 

Please see the below the aspects that you should consider:

8.2.3 Part A - Group work, small groups of 2 to 3 students.  (30%)

1.      Data Audit and Preparation of Data                                             (30 marks)

1.1.   Describe your data (give an overview summary of your data set)

1.2.   Select a suitable set of data (will you use a subsets of the data or the entire datasets)

1.3.   Identify your input and class variables (which variable are you going to use as your class variable)

1.4.   Analyse your variables (for each variable, you need to discuss the variable type, calculate relevant summary statistics and visually display the data)

1.5.   Discuss any anomalies in the data (for each variable you need to discuss missing values, outliers etc.)

1.6.   Discuss and carry out the appropriate handling of any anomalies identified in section 1.5

1.7.   Carry out appropriate pre-processing/transformations of the data set

8.2.4 Part B - Individual work (70%) – (Report) (using association rule )

2.      Data Analysis and Results                                                                       (40 marks)

2.1.   Initial Analysis using Tableau (using diagrams/graphs to highlight important variables)

2.2.   Further analysis using Data Mining Algorithms using a suitable software package (i.e. WEKA ).  Each student is expected to concentrate on one (or possibly two) algorithms and the algorithms used by each member of the group should be different.

2.3.   Final analysis using Tableau based on finding in 2.1 and 2.2.

2.4.   Displaying results using dashboards in Tableau

2.5.   Discussion and interpretation of result

3.      Conclusion                                                                                                          (15 marks)

3.1.   A comparison of the results obtained by different members of the group.

3.2.   A discussion of the overall results (e.g. What were the important findings? Which algorithms produced the best results? Etc.)

3.3.   A discussion of the business intelligence that can be obtained from these results.

Presentation and Viva (As a group with each group member contributing individually)