Comp3340 Data Mining For Principal Assessment Answers
Questions:
b. Briefly explain advantages and any disadvantages of using the PCA compared to other methods for this task.
Answers:
Principal Component Analysis
Selection of the Variables
- To perform Principal Component Analysis, from the Data Analysis tab choose the option Transform. From the transform option, selct Principal Components.
- The first step in the Principal Component Analysis is the selection of the variables. The window for the selection of the variables which is the first step in the Principal Component Analysis is shown below.
- In the worksheet option, the name of the worksheet is displayed. In the workbook option, the name of the excel file is displayed. The range from which column to which column with row number is specified.
- Number of rows and number of columns is specified.
Component Selection
The principal Component Analysis provides two types of components known as Fixed components and smallest components. Fixed components is selected. From the two provided methods, Use Correlation Matrix with Standardized variables is selected.
Principal Component Score
- The last step in the Principal Component Analysis is the selection Data Transformation and Data Fault Detection.
- In the data transformation tab, click the check box called Show principal component score to finish the principal Component Analysis.
- After selecting the required option, click on the Finish button.
- Thus, the Principal Component Analysis steps are completed.
Results of the Principal Component Analysis
After completing the three steps of the Principal Component Analysis. Two worksheets are shown one is the output sheet for Principal Component Analysis called as PCA_Output and the another one is the Scores of the Principal Component Analysis named as PCA_Scores. In the PCA_Output worksheet, inputs of the principal component analysis that is being given is displayed. Below the input specification, the principal components are specified. 8 components are specified for each variables from X1 to X8. Eigen value, Variance in percentage and Cumulative variance in percentage are determined in the Explained variance area. In the PCA scores worksheet, the scores are provided for all the records of the worksheet with all the 8 components.Advantages of Principal Component Analysis
- The Principal Component Analysis is capable of partitioning the data with variance into a discrete image set.
- This Analysis is considered to be the most effective one as compared to other data analysis tools.
- The PCA has the advantage to minimise the number of dimensions without much lose in the information (Sang, Wang and Cao, 2017).
Disadvantages of Principal Component Analysis
- The variables that has the high absolute values of variances do not dominate the first principal component.
- The PCA is not a sufficient analysis for the variables that are not linearly coordinated.
- The mean and variance values that are produced by the PCA do not determine any relative information of some distributions (Quora, 2017).
(i) P(CC=1|Loan=1)
The persons who are actively using credit card and who is ready to accept the offer for Loan is taken and probability is calculated for them. The Persons who are using credit cards actively and ready to accept the loan is totally 70.96%.
(ii) P (Online=1 | Loan=1)
The persons who are active in online bank transactions and ready to accept the offer for Loan is considered. 61.9% persons who are active in online bank transactions are ready to accept the Loan offer.
(iii) P (Loan=1)
The Persons who are ready to accept the loan is 10.24%
(iv) P (Online=1 | Loan=0)
The persons who are active in online transactions but not interested to accept the Loan offer is taken to calculate the probability. 38.1% is not ready to accept the offer for Loan.
(v) P (Loan=0)
The persons who are not ready to accept the loan is 89.76%.
Best Possible Strategy to get the Loan
The pivot tables are created for many attributes with different values in the Universal bank data set. Among all possible strategies, the third strategy that is the calculation of the Probability in which Loan as a function of the credit card and taking the Loan as a function of Online is considered to be the best strategy. The Strategy takes all the possible values of the variables called Online ( The persons who are active and not active in online bank transactions), Loan (The persons who are ready to accept the offer for Loan and who are not willing to accept the Loan offer) and the last variable is that the Credit Card (The persons who actively use the credit card and who do not use credit cards). These variables are the significant variables to find the Loan acceptors. The strategy also makes easy if they find credit card users are willing to accept the Loan offer, then the bank can approach the persons to use credit cards by providing them many offers in the credit card. If the bank finds that the persons who do online transactions more is ready to accept the Loan offer, then the bank can encourage their account holders to do more online transactions by creating awareness on using the online transactions in an active way. Special care can be taken by the bank to create awareness to the persons who do not know to use online transaction. So by interacting with customers regarding credit cards and online transactions, the bank can get an idea that who will accept the personal loan that is offered by the bank and other financial institutions.
References
Dobashi, K. (2017). Automatic data integration from Moodle course logs to pivot tables for time series cross section analysis. Procedia Computer Science, 112, pp.1835-1844.
Quora. (2017). Limitations of the Principal Component Analysis. [online] Available at: https://www.quora.com/What-are-some-of-the-limitations-of-principal-component-analysis [Accessed 15 Sep. 2017].
Sang, P., Wang, L. and Cao, J. (2017). Parametric functional principal component analysis. Biometrics.
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