ITECH1103 Assignment 2 - Analytics Report

Overview 

• The purpose of this task is to provide students with practical experience in writing a data analytical report to provide useful insights, pattern and trends in a chosen dataset in the light of  a set of tasks required within this document. This dataset will be chosen from the UC Irvine  Machine Learning Repository1. This activity will give students the opportunity to show  innovation and creativity in applying the WEKA data mining software, and designing useful visualization and data mining solutions presented as an analytics report. 

Timelines and Expectations 

Percentage Value of Task: 35% 

Due: Week 11 

Minimum time expectation: Preparation for this task will take approximately 40 hours 

Project Details 

You will use an analytical tool (i.e. WEKA) to explore, analyse and visualise a dataset of your  choosing. An important part of this work is preparing a good quality report, which details your  choices, content, and analysis, and that is of an appropriate style. 

The dataset should be chosen from the following repository: 

UC Irvine Machine Learning Repository 

https://archive.ics.uci.edu/ml/index.php 

The aim is to use the data set allocated to provide interesting insights, trends and patterns  amongst the data. Your intended audience is the CEO and middle management of the  Company for whom you are employed, and who have tasked you with this analysis. 

Tasks 

Task 1 - Data choice. Choose any dataset from the repository that has at least five attributes, and  for which the default task is classification. Transform this dataset into the ARFF format required by  WEKA. 

Task 2 - Background information. Write a description of the dataset and project, and its  importance for the organization. Provide an overview of what the dataset is about, including from  where and how it has been gathered, and for what purpose. Discuss the main benefits of using data  mining to explore datasets such as this. This discussion should be suitable for a general audience.  Information must come from at least two appropriate sources be appropriately referenced. 

Task 3 - Data description. Describe how many instances does the dataset contain, how many attributes there are in the dataset, their names, and include which is the class attribute. Include in  your description details of any missing values, and any other relevant characteristics. For at least 5  attributes, describe what is the range of possible values of the attributes, and visualise these in a  graphical format. 

Task 4 – Data preprocessing. Preprocess the dataset attributes using WEKA's filters. Useful  techniques will include remove certain attributes, exploring different ways of discretizing continuous  attributes and replacing missing values. Discretizing is the conversion of numeric attributes into  "nominal" ones by binning numeric values into intervals2. Missing values in ARFF files are  represented with the character "?"3. If you replaced missing values explain what strategy you used to select a replacement of the missing values. Use and describe at least three different preprocessing techniques. 

Task 5 – Data mining. Compare and contrast at least three different data mining algorithms on your  data, for instance:. k-nearest neighbour, Apriori association rules, decision tree induction. For each  experiment you ran describe: the data you used for the experiments, that is, did you use the entire  dataset of just a subset of it. You must include screenshots and results from the techniques you  employ. 

Task 6 – Discussion of findings. Explain your results and include the usefulness of the  approaches for the purpose of the analysis. Include any assumptions that you may have made about  the analysis. In this discussion you should explain what each algorithm provides to the overall  analysis task. Summarize your main findings. 

Task 7 – Report writing. Present your work in the form of an analytics report. 

Submission 

The assignment is to be submitted via the Assignment submission box in Moodle. This can be found in  the Assessments section of the course Moodle shell. Your report file will be submitted as either a MS  word file or a PDF. If you are using MacOS, please submit as a PDF. 

Your report will include the following in the order provided below: 

A cover page with your name and student ID 

Table of Contents 

Table of Figures / Tables 

Data choice 

Background information 

Data description 

Data preprocessing 

Data mining 

Discussion of findings 

References 

Appendices 

Your references should use the APA referencing style; information is available here: https://federation.edu.au/library/student-resources/help-with-referencing 

https://federation.edu.au/library/student-resources/fedcite 

Identify all sources of information used. You are reminded to read the “Plagiarism” section of  the course description. 

A passing grade will be awarded to assignments adequately addressing all assessment  criteria. Higher grades require better quality and more effort. For example, a minimum is set  on the wider reading required. A student reading vastly more than this minimum will be better  prepared to discuss the issues in depth and consequently their report is likely to be of a  higher quality. So before submitting, please read through the assessment criteria very  carefully.