UnitAssessment TypeAssessment NumberAssessment NameWeightingAlignmentwith Unitand CourseDue Date and TimeGroup AssignmentA4Data Mining & BI Report25%ULO1, ULO2, ULO3, ULO4Assessment Description In this assessment, the students will extend their previous work from assessment A3Business case understanding. Here, the students have to submit a report of the datamining process on a real-world scenario and a presentation and QA Session will be heldbased on the report written. The report will consist of the details of every step followed by thestudents.Detailed SubmissionRequirementsCover Page• Title• Group membersIntroduction• Importance of the chosen area• Why this data set is interesting• What has been done so far• Which can be done• Description of the present experiment1. Data preparation and Feature extraction:1.1 Select datao Task Select data1.2 Clean datao Task Clean datao Output Data cleaning report1.3 Construct data/ feature extractiono Task Construct datao Output Derived attributeso Activities: Derived attributeso Add new attributes to the accessed datao Activities Single-attribute transformationso Output Generated recordsReport (10%): Week 11, Friday, 04 June 2021, 11:59 pm viaMoodle. Presentation and QA Session (15%): Week 12 In Class.2 Modeling2.1 Select modeling techniqueo Task – Select Modelling Technique2.2 Output Modeling techniqueo Record the actual modeling technique that is used.2.3 Output Modeling assumptiono Activities Define any built-in assumptions made by the technique aboutthe data (e.g. quality, format, distribution). Compare these assumptionswith those in the Data Description Report. Make sure that theseassumptions hold and step back to the Data Preparation Phase ifnecessary. You can explain the data file here, even when it is preprepared.3 Generate test design3.1 Task Generate test designo Activities Check existing test designs for each data mining goalseparately. Decide on necessary steps (number of iterations, number offolds etc.). Prepare data required for test. (You can use 66% of recordsfor model Building and rest for Testing)3.2 Build modelo Task – Build modelRun the modeling tool on the prepared dataset to create one or moremodels. (Using Knime Tool as shown in the lab).3.3 Output Parameter settingso Activities – Set initial parameters. Document reasons for choosing thosevalues.o Activities – Run the selected technique on the input dataset to producethe model. Post-process data mining results (e.g. editing rules, displaytrees).3.4 Output Model descriptiono Activities – Describe any characteristics of the current model that maybe useful for the future. Give a detailed description of the model andany special features.o Activities – State conclusions regarding patterns in the data (if any);sometimes the model reveals important facts about the data without aseparate Assessment process (e.g. that the output or conclusion isduplicated in one of the inputs).4 Evaluation and ConclusionPrevious evaluation steps dealt with factors such as the accuracy and generality of themodel. This step assesses the degree to which the model meets the businessobjectives and seeks to determine if there is some business reason why this model isdeficient. It compares results with the evaluation criteria defined at the start of theproject. A good way of defining the total outputs of a data mining project is to use theequation:RESULTS = MODELS + FINDINGSIn this equation we are defining that the total output of the data mining project is notjust the models (although they are, of course, important) but also findings which wedefine as anything (apart from the model) that is important in meeting objectives of thebusiness (or important in leading to new questions, line of approach or side effects(e.g. data quality problems uncovered by the data mining exercise).Note: although the model is directly connected to the business questions, the findingsneed not be related to any questions or objective, but are important to the initiator ofthe project.~ End of Assessment Details ~Marking CriteriaActivities Rank the possible actions. Select one of the possible actions. Documentreasons for the choice. ContentMarksCover PageTable of contents0.5Executive Summary0.5Introduction0.5Data Pre-processing and feature extraction2.5Experiment3Result analysis2.5Conclusion0.5Presentation and QA15 RubricsMarking criteriaHDDCPFULO1: Demonstrate broadunderstanding of datamining and businessintelligence and theirbenefits to businesspracticeULO 2: Choose and applymodels and key methodsfor classification,prediction, reduction,exploration, affinityanalysis, and customersegmentation that can beapplied to data mining aspart of a businessintelligence strategyULO3: Analyse appropriatemodels and methods forclassification, prediction,reduction, exploration,affinity analysis, andcustomer segmentation todata miningULO4: Propose a datamining approach using realbusiness cases as part of abusiness intelligencestrategyReport,presentationand QAoutcomeaddress all thetasks.Reportconsists ofno/minormistakes.(21-25 marks)Report,presentationand QAoutcomeaddress all thetasks.Report consistsof a few numberof mistakes.(18-20 marks)Report,presentationand QAoutcomeaddress most ofthe contents.Report consistsof a few numberof mistakes.(15-17 marks)Report,presentationand QAoutcomeaddress a few ofthe contents.Report consistsof a goodnumber ofmistakes.(13-14 marks)Incompletereport.Unable toperform theexperiment/data preprocessing/conclude result.Unable toanswer to thequestion of QASession andUnable topresent thework that hasbeen done.(0-12.5 marks) Misconduct • Engaging someone else to write any part of your assessment for you is classified asmisconduct.• To avoid being charged with Misconduct, students need to submit their own work.• Remember that this is a Turnitin assignment and plagiarism will be subject to severepenalties.• The AIH misconduct policy and procedure can be read on the AIH website(https://aih.nsw.edu.au/about-us/policies-procedures/).Late Submission • Late submission is not permitted, practical submission link will close after 1 hour.Special consideration • Students whose ability to submit or attend an assessment item is affected by sickness,misadventure or other circumstances beyond their control, may be eligible for specialconsideration. No consideration is given when the condition or event is unrelated to thestudent’s performance in a component of the assessment, or when it is considered notto be serious.• Students applying for special consideration must submit the form within 3 days of thedue date of the assessment item or exam.• The form can be obtained from the AIH website (https://aih.nsw.edu.au/currentstudents/student-forms/) or on-campus at Reception.• The request form must be submitted to Student Services. Supporting evidence shouldbe attached. For further information please refer to the Student Assessment Policy andassociated Procedure available on(https://aih.nsw.edu.au/about-us/policies-procedures/).
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