BIS3001 - Data Analytics for Business - Report Writing - Data Analytics Assignment Help

Assignment Task

Task 1: Data Warehouse Report
XYZ company has a data warehouse supporting their business. Using this warehouse system, the company has successfully maintained its customer information and enhanced their business. However, the company currently is considering moving real-time data warehouse producing more freshers data. This company pointed James to help them implement a customer self-service system that requires fresher data than is presently available in the data warehouse. James wants some support in developing this system. James has been informed about Enterprise application integration (EAI) and Enterprise information integration (EII) technologies and wonders how they might implement it. In particular, he has the following questions:

a. What are Enterprise application integration (EAI) and Enterprise information integration (EII) technologies?
b. How are EAI and EII related to extract, transform, load (ETL)?
c. How are EAI and EII related to Relational Data Warehouse (RDW)?
d. Are EAI and EII required, complementary, or alternatives to RDW?

Write a short discussion report based on the above questions.

Task 2: Data Mining Report
In this task, you are required to read the journal articles provided below and write a short discussion report based on the points below:
• Identify the major data mining challenges include efficiency, scalability security, and privacy.
• Some of the challenges have been addressed in recent data mining research, evaluate the effect these challenges are on the business sector.
• Support your response with proper examples and references.
• The report follows a referencing style that complies with the APA style, and the in-text citations are made purposefully.

Task 3: Create and explore Weka data file of type ARFF
Download a text file called HSbank_data.csv from the subject site (Canvas) and open it using a text editor such as WordPad, Notepad++ etc. for windows system or Textedit for Mac. You need to explore and convert this file into an ARFF file for Weka. The text file you will be using contains a sample of real-life data related to HS bank customers. The HSbank_data.csv file is not entirely formatted as a Weka file (ARFF). This file has some formatting errors, and your task is to find these errors and fix them to have a valid ARFF file. Save the valid file as HSbank_data.arff. Explore the HSbank_data.arff dataset using Weka Explorer and answer the following questions.
Make sure to include screenshots of the visualisations to support your answers.

1. Take a screenshot of your corrected ARFF file.
2. Which attribute in the dataset do you think is useless and did not provide useful information for prediction?
3. How many attributes the dataset has?
4. How many instances the dataset has?
5. What is the class attribute HSbank_data.arff dataset?
6. What proportion of customers who has a mortgage and living in Inner City?
7. What proportion of customers who has a mortgage and living in Inner City?
8. What proportion of customers who has a mortgage and their income between $5000 and $31000? 
9. How many customers are married and has no mortgage?
10. How many customers have not owned a car and has a mortgage?

Task 4: Practical Analysis
Use the HSbank_data dataset from Task 1 to perform data mining tasks for Task 2. Each question worth 3 marks.and compare the performance on this data set for the following classification algorithms using classification algorithms:
• Naive Bayes
• HoeffdingTree
• SVM ( or SMO)
• J48

Write a summary report that compares the performance of these algorithms. Make sure to comment on these algorithms performance and accuracy using the performance metrics shown in the classifier output, such as the confusion matrix, etc. In your report, you need to state if there is a difference in the performance between these algorithms and which algorithm performs best. Make sure to include the necessary tables, graphs, screenshots etc. to make your report understandable to the person who reads.

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