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COIS13013 Business Intelligence

Question

Answered

Task

Objectives

This assessment item relates to course learning outcomes numbers 1, 2, 3, and 6 as stated in the unit profile.

Part A

Data driven decision making (DDDM) is a process working towards key business goals by leveraging verified, analysed data to build BI applications. Answer the following questions in Part A.

Question 1 (4 marks)

What are the main data processing steps for data preparation? Briefly describe each step and provide a relevant example (limited to 500 words).

Question 2

a)Briefly discussed main difference between the data mining tasks of classification and clustering. (2 marks)
 
b)For the following example, calculate the accuracy, precision, recall: (2 marks)
 
Note: Spam is a positive class (y =1) and “Not Spam” is a negative class (y=0) for a binary classifier.
 

Spam

1

Not Spam

0

Not Spam

0

Spam

1

Spam

0

Spam

1

Not Spam

0

Spam

1

Spam

0

Not Spam

1

Question 3 (4 marks)

Given a database of transactions where each transaction is a collection of items purchased by a customer in a visit. Generate frequent item set with minimum support requirement minsup=30%

Transaction No

Items

1001

Beef, Chicken, Milk

1002

Beef, Cheese

1003

Cheese, Fish

1004

Beef, Chicken, Cheese

1005

Beef, Chicken, Clothes, Cheese, Milk

1006

Chicken, Clothes, Milk

1007

Chicken, Milk, Clothes

Find all frequent itemsets using Apriori Algorithm (Show all intermediate results).

Part B (15 marks)

The goal of this question is to gain practical experience in applying classification to real data. There are two tasks for this question: data preparation and classification using decision trees using R or Python or any tools you can to achieve generating decision tree.

Task 1: Data Preparation

1.1 Extract data into R data frame

1.2 Assign the following names to the five different columns in your dataset

1. sepal length in cm

2. sepal width in cm

3. petal length in cm

4. petal width in cm

5. class

•Iris Setosa

•Iris Versicolour

•Iris Virginica

1.3Remove all rows with missing values

1.4Save the dataframe into a file with filename Iris_processed.Rda

Task 2: Decision Trees

2.1 Load the pre-processed data from task 1 into the data frame

2.2 Set seed 2020

2.3Divide the dataset into training and test subsets randomly (70% and 30% respectively)

2.4Generate a classification tree and visualise 

2.5 Provide a summary of the classification result

Part C

Question 5

Search a business intelligence dashboard on the Internet, similar to the following examples:

Review the available options or views to complete the following tasks:

•What characteristics the Business Intelligence Dashboard in organisation?

•How should the Business Intelligence Dashboard be designed to facilitate decision making?

•To critique the above question, you should use five additional references, apart from the prescribed textbook.
 
Note: You should include the BI dashboard with your answers. (Maximum 1000 words)

COIS13013 Business Intelligence

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