ITECH1103 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.

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.

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. The information must come from at least two appropriate sources be appropriately referenced.

Task 3 - Data description. Describe how many instances 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.