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ASSESSMENT TASKS

Task 1 FORMATIVE TASK k-means Clustering in Principle

FORMATIVE TASK

Instruction: Produce a briefing document for your boss to explain k-means clustering and its potential uses in your organisation. The report must contain the following:

• An outline of what is understood by k-means clustering, and the steps involved in the k- means clustering
• Identify and explain how to determine the optimal number of clusters ‘k’ by using the elbow method, and how to interpret SSE and WSS.

• Discuss the potential uses of k-means clustering within your organisation, and how it might help promote the business of the

Task 2 SUMMATIVE TASK k-means Clustering in Practice

SUMMATIVE TASK

Instruction: Undertake a k-means clustering for your organisation and write a report on it. Your report must contain the following:

• An outline of how Python was used to create an accurate k-means algorithm, including an explanation of key terms such as Inertia and Silhouette Score. (LO 2.1, 3.1)
• Identify and explain how accurate visualisation of the clusters generated was carried out, and how the Inertia and Silhouette score were interpreted. (LO 2.2, 2.1, 3.2)
• A judgment as to the conclusions that can be drawn from the use of the algorithm, and the accuracy of these conclusions in making future decisions (LO 2.2, 3.3)

Learning Outcomes: To achieve this unit, the learner must be able to:	Assessment Criteria: Assessment of these learning outcomes will require a learner to demonstrate that they can:
2. Understand how to evaluate         k-means clusters	2.1                                     Define: –          Inertia –          Silhouette Score 2.2           Explain  how   to   interpret  Inertia  and Silhouette score
3. Be able to create and evaluate a k-means model.	3.1           Use Python to build an accurate k-means model. 3.2           Use Python to create accurate visualisations of the clusters generated by the k-means clustering algorithm. 3.3           Use Python to evaluate the accuracy of a k– means model.