Apply Standard Processes to Prepare Large Data Sets for Data Exploration - Management Assignment Help

Assignment Task:

Task:

Project 2 Assessment description

It is a common practice to apply multiple methods to build classification models so that these models back each other and give better and more convincing prediction results. For Project 2, you will continue with the work you carried out in Project 1 on the given dataset. You are advised to incorporate the feedback you received for Project 1 in your current task. You will submit a well-structured report with a brief abstract, an introduction, components identified in the instruction section, and a conclusion. The assessment aims The aim of this assignment, like the previous, is to allow you to assume the role of a professional Data Analyst to explore a client’s dataset for predictive purposes. In this task, however, you will learn how to: (a) exploit results of relevant data inspection and pre-processing in Project 1 for building classifiers;

(b) employ different classification methods for building accurate models for prediction, and

(c) perform formal evaluations of competing models from different classification techniques to choose the best performing one(s). You will also develop your ability to communicate your findings to both experts and non-experts through report writing.

By completing this assessment you will meet the following course learning objectives:

CO1. Apply standard processes to prepare large data sets for data exploration.

CO2. Perform data exploration on large data sets using visualisation, statistical techniques, and data mining techniques to identify relationships and opportunities.

CO3. Develop accurate descriptive and predictive models based on large data sets.

CO4. Perform predictive analytics on large data sets using an industry standard software tool-set.

Assessment criteria

? Present a summary of the variable inspection, treatment, feature selection, binning etc. from Project 1 in no more than two pages.

? Apply the methods of Decision Tree, kNN, Naïve Bayes, Neural Network, and SVM to the given dataset.

? Show the performances of the models using misclassification rate, precision, recall, F measure, and any other measures applied for models.

? Report on the best model that represents the method.

? Compare the performances of different methods using the above measures plus ROC curve, lift chart etc. and indicate reasons why a method performs worse or better than other methods.

? Present the discussion in a well-structured report: written expression is error free (spelling, grammar and language). Where appropriate, UniSA Harvard referencing convention is employed to cite information that is taken from external sources.

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