Reference no: EM133665087

Intelligent Systems for Analytics

Objective

Create DASHBOARD and present your insights including some basic analytics and four different visualisations. The student can use any software to create the dashboard such as Microsoft excel, Power BI, Tableau, etc.

This is now an individual assignment, not a group assignment.

You need to record your presentation, 3-6 minutes. You and your slides should be clear in the video file.

Submit your video file of your presentation in the provided link by the due date. Only original file will be accepted; a link to your video file will not be marked.

Presentation Requirements

The presentation must include,

Brief description of the dataset and the reference link
Method of data acquisition to the software
Create four different visualizations to the dashboard
Summary of the visualizations and how they could be used in predictive decision making

Introduction

This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to give students experience in researching a topic and writing a report relevant to the Unit of Study subject matter.

Task
For this component you will prepare a report or critique on an academic paper related to Intelligent Systems for Analytics or Intelligent Systems.

Some possible topic areas include but are not limited to:
Intelligent Systems for Data Warehouse systems
Evolving Intelligent Systems: Methods, Learning, & Applications
Distance Metric Learning in Intelligent Systems
Intelligent Systems for Socially Aware Computing
Data Mining techniques with IS
frameworks for integrating AI and data mining
Expert System
Structure of knowledge Engineering
IS and Support Vector Machines
IS and Neural Network Architectures
Heuristic Search Methods
Genetic Algorithms and Developing GA Applications

The paper can be from any academic conference or other relevant Journal or online sources such as Google Scholar, Academic department repositories etc. All students must select a different paper. You can discuss with your lecturer before week 6 to decide on a topic. The topic needs to be chosen before week
Students may discuss their chosen topics/papers in discussion forum and may avoid similar paper selected by multiple students. The paper you chose should be published in the last 6 years (must be published 2017 or after). Note: popular magazine articles, web sites and blogs are not academic sources.

Your report should be limited to approx. 1500 words (not including references). Use 1.5 spacing with a 12-point Times New Roman font. Though your paper will largely be based on the chosen article, you can use other sources to support your discussion. Thus, citation of sources is mandatory and must be in the IEEE style.

Report Content

Title Page: The title of the assessment, the name of the paper you are reviewing and its authors, and your name and student ID.
Introduction: A statement of the purpose for your report and a brief outline of how you will discuss the selected article (one or two paragraphs). Make sure to identify the article being reviewed.
Body of Report: Describe the intention and content of the article. Document a critical analysis and clearly identify the workflow of the business process, the organizational units, actors, process relationship relevant to your chosen paper. Moreover, critically describe the adopted business process model, method and/or business process management tool which has been developed and applied in your chosen paper. In addition to that, report the approach to diagnose the root causes of poor process performance and recommend appropriate managerial levers for improving them. If such analysis and recommendation are not outlined in your chosen paper, discuss and justify your own view.

Conclusion: A summary of the points you have made in the body of the paper. The conclusion should not introduce any ‘new' material that was not discussed in the body of the paper. (One or two paragraphs)
References: A list of sources used in your text. They should be listed alphabetically by (first) author's family name. Follow the IEEE style.
The footer must include your name, student ID, and page number.

Introduction

This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to improve student collaborative skills in a team environment and to give students experience in constructing a range of documents as deliverables form different stages of the Intelligent Systems for Analytics.
Task
This assignment is to be completed in teams of 3 or 4 students. You should begin by submitting (at the end of week 7) the signed group participation form provided in the Moodle. This form needs to be completed and signed by all group members. Once submitted, the teams will remain unchanged, and no member additions or deletions will be allowed unless by approval of your subject coordinator. Any person not part of a group by the end of week 7 will be assigned randomly to a group by your lecturer.

Carefully read the following two questions and provide the appropriate answer.

Question 1:

The Diabetes prediction dataset comprises medical and demographic records of patients, alongside their diabetes status (positive or negative). It encompasses diverse attributes such as age, gender, body mass index (BMI), hypertension, heart disease, smoking history, HbA1c level, and blood glucose level. This dataset facilitates the creation of machine learning models aimed at forecasting diabetes occurrence based on patients' medical backgrounds and demographic particulars.

Your goal is to use different classifiers to build a training model based on training data points and then test its performance on test data points.

Students must use the following classifiers. The selection of the classifiers depends upon the members of the group, e.g., if the group has four members, then they will use the four classifiers from the following five classifiers.
Neural network
Support vector machine
Nearest Neighbour algorithm
Decision tree
Naive Bayes

The group must prepare a report which include the followings:

Explain the process of building each classifier using the training dataset (add the screenshots).
Create the confusion matrix based on training/ testing.
Explain how you evaluated the classifier.
Predict the category of the values in table used for Testing set.
Compare the results between the different classifiers and discuss which one is the best and why.

Note: Students can use any open-source free data mining software such as Python, Statistica Data Miner, Weka,RapidMiner, KNIME and MATLAB etc.

Question 2:
Create a DASHBOARD. For creating a dashboard, the group can use the same Diabetes prediction dataset (See the attachment in LMS). The group has to prepare a report which include the followings:
Write an introduction about the fields used in the dataset.
Create at least four figures (different graphs) and add them to dashboard.
Add Screenshot of each of the steps.
Describe the figures in the dashboard.
Note: Students can use any software to create the dashboard such as Microsoft excel, Power BI, Tableau, etc.