Data Mining and Visualization for Business Intelligence Assignment

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Added on 2020-03-16

Data Mining and Visualization for Business Intelligence Assignment

Added on 2020-03-16

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Data Mining and Visualization for Business Intelligence Assignment_1

1.1Association Rules........................................................................................................................21.1.1I)...........................................................................................................................................21.1.2II)..........................................................................................................................................31.1.3III).........................................................................................................................................41.2Cluster Analysis............................................................................................................................41.2.1A).........................................................................................................................................41.2.2B)..........................................................................................................................................41.2.3C)..........................................................................................................................................41.2.4D).........................................................................................................................................51.2.5e)..........................................................................................................................................51.1Association Rules

Data Mining and Visualization for Business Intelligence Assignment_2

1.1.1I)Asshown in the above table Antecedent is an observation found in the data.Similarly consequent is an item which is bought together along with antecedent. Inthis case Rule 1 states brushes and congealer are brought together by a customer, thenit can be said with 80% confidence that Nail Polish & bronzer will also be bought bythe same customer. As the table above shows Burshes, Concealer and Nail Polsih &Bronzer are bought together 77 times. On the other hand the support for C is only 103times .In other words the customer who buys Nail Polish & Bonzer also boughtBrushes. Similarly results also show that 62 times the The event A & C happened together.Furthermore the lift ratio shows that the likelihood of purchasing Brushes, Concealer,and Nail Polish & Bronzer as compared to the all transactions as whole.Similarly the Rule 2 states when the customer buy Nail Polish & Bronzer, they alsobuy Brushes & Concealer with the support for event A happening is 103 whilesupport for event C happening is 77. The confidence level for the rule is very low.Rule 3 states that when a customer buy nail polish, concealer & bronzer together thenthey also buy brushes with confidence of 81%. (Gupta, Garg, & Sharma, 2014; Rajak& Gupta, 2008; Sujatha & CH, 2011)1.1.2II)To see whether the rules generated from the association rules are efficient, there are multiplecriteria. Firstly we need to look into the confidence level which gives shows the confidence ofthat rule. Also, it should be logical & backed by the business understanding. For example, theRule number 6 has Confidence level more than 80% & the lift ratio is 3.7. Also, this rule makesbusiness sense. Hence, this rule can be considered as efficient rule to apply.

Data Mining and Visualization for Business Intelligence Assignment_3

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