COIS13013: Data Driven Decision Making (DDDM) - Difference Between the Data Mining Tasks of Classification and Clustering - IT/Computer Science Assignment Help

Assignment 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, analyzed data to build BI applications. Answer the following questions in Part A.

Question 1

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

Question 2

1. Briefly discussed main difference between the data mining tasks of classification and clustering. (2 marks)

2. 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.

Question 3  

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%

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

Part B

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

1.3 Remove all rows with missing values

1.4 Save 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

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

2.4 Generate 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: 

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