Data Mining & Business Intelligence Assessment Type Report Assessment Number 3 Assessment Weighting Data Mining & BI Report + Presentation 40% Alignment with Unit and Course Unit Learning Outcome ULO1: Demonstrate broad understanding of data mining and business intelligence and their benefits to business practice. ULO2: Choose and apply models and key methods for classification, prediction, reduction, exploration, affinity analysis, and customer segmentation that can be applied to. ULO3: Analyse appropriate methods for classification, prediction, reduction, exploration, affinity analysis, and customer segmentation to data mining. ULO4: Propose a data mining approach using real business cases as part of a business intelligence strategy. ULO5: Propose a data mining approach using real business cases as part of a business intelligence strategy. Graduate Attributes Assessed GA 1: Communication GA 2: Collaboration GA 3: Research GA 4: Critical Thinking GA 5: Ethical Behaviour GA 6: Flexibility Assessment Description In this assessment, the students must submit a report of the data mining process on a realworld scenario and a presentation and QA Session will be held based on the report written. The report will consist of the details of every step followed by the students. Cover Page and Table of Contents (1 Mark)  Title  Group Members Executive Summary (1 Mark) 1. Introduction (1 Mark)  Importance of the chosen area  Why this dataset is interesting  What has been done so far  What can be done  Description of the present experiment 2. Data preparation/pre-processing and feature extraction (3 Marks) 2.1 Select Data  Task: Select data 2.2 Clean Data  Task: Clean data 2.3 Construct data/feature extraction  Task: Construct data  Output: Derived attributes  Activities: Derived attributes  Add new attributes to the accessed data if required  Activities: Single-attribute transformations 3. Experiment (9 Marks) You must choose a previously selected public dataset for A3 from the websites mentioned in page 1. Select three experiments (3 marks each) from the list (your Lecturer may choose one for you): A. Build a simple classifier apply to dataset (Decision Tree) B. Cluster Analysis (K-Means) C. Topic Detection Analysis (Import public post comments from Twitter, Facebook, Instagram with the help of exportcomments.com). D. Linear Regression. Additional experiments may carry some bonus marks, talk to your Lecturer. 3.1 Select Modelling Technique  Task: Select Modelling Technique 3.2 Output Modelling Technique  Record the actual modelling technique that is used. 3.3 Output Modelling Assumption  Activities: Define any built-in assumptions made by the technique about the data (e.g., quality, format, distribution). Compare these assumptions with those in the Data Description Report. Make sure that these assumptions hold and step back to the Data Preparation Phase if necessary. You can explain the data file here, even when it is pre prepared. 3.4 Generate Test Design  Activities: Check existing test designs for each data mining goal separately. Decide on necessary steps (number of iterations, number of folds etc.). Prepare data required for test. (You can use 66% of records for model Building/Training and rest for Testing). 3.5 Build a Model  Task: Build a model. Run the modelling tool on the prepared dataset to create one or more models. (Using Knime Tool as shown in the lab). 3.6 Output Parameter Settings  Activities: Set initial parameters. Document reasons for choosing those values.  Activities: Run the selected technique on the input dataset to produce the model. Postprocess data mining results (e.g., editing rules, display trees). 3.7 Output Modelling Technique  Activities: Describe any characteristics of the current model that may be useful for the future. Give a detailed description of the model and any special features.  Activities: State conclusions regarding patterns in the data (if any); sometimes the model reveals important facts about the data without a separate Assessment process (e.g., that the output or conclusion is duplicated in one of the inputs). 4. Result Analysis / Evaluation (4 Marks) Previous evaluation steps dealt with factors such as the accuracy and generality of the model. This step assesses the degree to which the model meets the business objectives and seeks to determine if there is some business reason why this model is deficient. It compares results with the evaluation criteria defined at the start of the project. A good way of defining the total outputs of a data mining project is to use the equation: RESULTS = MODELS + FINDINGS In this equation we are defining that the total output of the data mining project is not just the models (although they are, of course, important) but also findings which we define as anything (apart from the model) that is important in meeting objectives of the business (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 findings need not be related to any questions or objective but are important to the initiator of the project. 5. Conclusion (1 Mark) Write a conclusion relevant to your project selection to final outcome, and future study/developments. Contents Marks Cover page and table of contents 1.0 Executive Summary 1.0 Introduction 1.0 Data pre-processing and feature extraction 3.0 Experiment 9.0 Result analysis 4.0 Conclusion 1.0 Report Total 20 Group Presentation 5 Individual QA Session (after presentation) 15 Presentation and QA Total 20 A3 Total 40 Detailed  Follow the Marking Criteria described in page 04.  Submission must be between 2800 - 3000 words (not including executive Submission summary, table of contents, tables, figures, or the reference list). Requirements   Retain the formatting of the template (11 font, 2.5 margins, 1.5 spacing) Use Harvard referencing including the reference list.  All students must submit the peer evaluation via relevant Moodle link before the assessment due date.  One member of the group must submit the assessment through the Assessment 4 Turnitin link on Moodle page for this unit. Group Work   In Session 2 or 3, groups are formed. All groups will fill out the Group Charter and submit to the lecturer.  Not more than 4 students per group.  In the case where one team member ceases contact and/or contribution to the group work, the group must notify and consult with the Lecturer as soon as possible.  All students will submit a Peer Evaluation for their group members at the end of the assessment.