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Data Mining Business Case Analysis Report

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Added on  2020-04-01

Data Mining Business Case Analysis Report

   Added on 2020-04-01

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DATA MININGBUSINESS CASE ANALAYSISSTUDENT NAME[Pick the date]
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Question 1a)The principal component matrix is arguably a critical aspect of the PCA and allows for theusers to identify the useful features by considering the eigen values which is indicated asfollows.It needs to be determined taking variances contribution of various principal components and thusdecide the number of principal components which are to be considered as part of the analysis.Further, after narrowing on the significant principal components, five principal components havebeen considered assuming the aim to account for 88% of the variance. Then based on eigen values highlighted in the principal component matrix, the two main featuresfor each of the principal components have been identified. Consideration needs to be given onthe highest magnitude which would be selected as highlighted below.PC1 – x1, x2 (Indicating financial aspects)PC2 – x6, x8 (Indicating operational aspects)1
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PC3- x3, x7 (Indicating generation mix and cost)PC4 –x1, x3 (Indicating fixed cost related aspects)PC5 – x1, x4 (Indicating financial performance)Also, it needs to be opined whether normalisation before PCA is required or not. Usually it isrequired in order to remove the noise effect produced by the scales whereby the variable havingthe largest magnitude of scales also would have the largest absolute value of variance and hencein PCA also, this one aspect would contribute in a big way to the variance thereby highlightingthe relative non-significance of the other variables. However, considering the PCA results in thenon-normalised form, it may be stated that the need for normalisation does not arise here due toscale not being too skewed for either of the variables.(b) The critical disadvantages and advantages are listed below:DisadvantagesUse to predict the structure of dataset Use to reduce the large set into fewer dimensional dataset Minimize the risk factor of over-lapping/fitting of dataMaximize the variances Represents data orthogonally which helps to examine the principal components magnitudes AdvantagesNot valid for dataset which have “non-linear relationship”The distributed principal component in the cloud point does not exhibit the exact directionbecause each component is having its own principal axis which is at 900 (right angle) with2
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