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Data Mining Business Case Analysis

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Added on  2020-04-01

Data Mining Business Case Analysis

   Added on 2020-04-01

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DATA MININGBUSINESS CASE ANALYSIS[Pick the date]STUDENT ID
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Question 1a)One of the key results of the PCA is the component matrix which represents the Eigenvalues related to each particular feature. The respective scores can be derived from theequation derived through the use of this matrix. Further, the respective significance of theprincipal components is reflected from the extent of total variance that it accounts for,which is on account of the higher importance being provided to components that lead tohigher variance. Further, the principal components which have very limited contribution tothe total variance are not considered significant and are ignored. The relevant output ofPCA analysis as obtained from XLMiner is reflected below.The first step is to identify the number of principal components that should be considered.Assuming that only 80% of the variance needs to be explained, the PCA matrix can shiftfocus to only the first four principal components while ignoring the rest. In order tounderstand which aspect is being captured by these principal components in relation to the1
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firm’s performance, it is imperative to identify the features that the significant principalcomponents focus on. This is apparent from the following analysis.With regards to the first principal component, the essential parameters based onhighest values are x1(income to debt) and x2(rate of return). Hence, this component isrepresentative of the financial returns.With regards to the second principal component, the essential parameters based onhighest values are x4(Annual Load Factor) and x8(Total fuel costs). Hence, thiscomponent is representative of the operational efficiency.With regards to the third principal component, the essential parameters based onhighest values are x3(Cost per unit) and x7(Percent nuclear). Hence, this componentis representative of the variable electricity cost.With regards to the fourth principal component, the essential parameters based onhighest values are x1(income to debt) and x3(Cost per unit). Hence, this component isrepresentative of the financial performance.NormalisationOne of the potential issues with PCA can be in the form of distorting effect of the scales ofthe given variables. Owing to this, it may so happen that a particular variable tends toundermine the importance of the others by ensuring that the first principal componentaccounts for a very high contribution to total variance. This is not the importance of thevariance but rather the large range of scale. In such cases, normalisation is done to convertvariables into normal form and hence ensure that the principal components significance canbe correctly established. It is not required here on account of the total variance matrix whereno domination of a single variable occurs.(b) The central advantages and limitations of principal component analysis is outlined below:2
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