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Data Mining and Visualization in Business Intelligence

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Added on  2020-04-07

Data Mining and Visualization in Business Intelligence

   Added on 2020-04-07

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Data Mining and Visualization forBusiness IntelligenceBusiness Case Analysis[Pick the date]Student id and anme
Data Mining and Visualization in Business Intelligence_1
Data Mining and Visualization for Business IntelligenceQuestion 1 DIMENSION REDUCTION (a)PCA Principal component analysis has been run through XLMiner and the output is shown below:XLMiner – PCA summary output 1
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Data Mining and Visualization for Business IntelligenceXLMiner –PCA principal component analysis 2
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Data Mining and Visualization for Business IntelligenceInterpretation of PCA output From the values of variance in the variance % table, it can be noted that about 85% of variancecome from principal components 1, 2, 3, 4 and 5. Further, only small portions come fromprincipal components 6, 7 and 8. Therefore, these components would be termed as noisecomponents and can be removed from the original principal matrix. The matrix would be termedas reduced principal matrix and is highlighted below:After analyzing the features of the above highlighted principal components, the most significantfeatures would be decided which are shown below:X2 - Principal component 1X6 - Principal component 2X7 - Principal component 3 X3 - Principal component 4 X4 - Principal component 5 After evaluating the variances of each of the significant feature variable the key significantcomponents are listed below:3
Data Mining and Visualization in Business Intelligence_4

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