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The main objective of this assessment task is to apply data exploration and feature engineering techniques to real-world business problems. Relevant Learning Objectives: •     Subject Learning Objectives: SLO 1

Assessment Task 1: Data Exploration

Objective: The main objective of this assessment task is to apply data exploration and feature engineering techniques to real-world business problems.

Relevant Learning Objectives:

•     Subject Learning Objectives: SLO 1

•     Course Intended Learning Outcomes: CILO D.1

Format:

•     Type: Report

•     Work: Group assignment, but each member will be individually assessed.

Weightage: 30% of the overall grade.

Task Description: Students are required to:

1.   Form. groups of 2-3 (you may increase group size at max of 5 members  based on your tutor’s choice) members.

2.   Select a dataset similar with the COMMSDATA (in SAS Viya Course) or any other

existing datasets is available for classification task. Selecting a right dataset is key in this assignment. Please ensure to select a large dataset (over 1000 data points).

 

3.   Select a predictive business analytics task based on the chosen dataset.

4.   Collaboratively analyse both the chosen business problem and its associated dataset.

5.   Submit a report, detailing:

o  The business problem they aim to solve.

o  Characteristics of the chosen dataset.

o  Data transformation processes applied.

o  Proposed method to address the data mining problem.

Additionally, the report should also describe:

•     The composition of the group.

•     Roles and responsibilities of each team member.

•     A proposal for addressing the data mining problem.

•     A comprehensive plan outlining how they intend to solve the problem.

Assessment Criteria: Assignments will be evaluated based on:

1.   Description of business problem

2.   Quality and feasibility of the proposal and plan.

3.   Data exploration and initial findings:

–      Quality of pre-processing

–      Quality of initial findings

4.   EDA Visualisation

Submission Details:

•     Format: Electronic copy

•     Platform. Canvas for report and SAS Viya (in Exchange Folder) for upload the pipeline

•     Maximum Length: 15 pages (using 11 or 12-point font)

•     Due Date: 11.59pm, Friday 8 September 2023

•     Feedback Timeline: Feedback with marks will be provided within 2 to 3 weeks after submission.

The main objective of this assessment task is to apply data exploration and feature engineering techniques to real-world business problems. Relevant Learning Objectives: •     Subject Learning Objectives: SLO 1
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