The datasetEducation - Post 12th Standard.csvcontains information on various colleges. The data dictionary of the 'Education - Post 12th Standard.csv' can be found in the following file:Data Dictionary.xlsx.

• Perform Exploratory Data Analysis [both univariate and multivariate analysis to be performed]. What insight do you draw from the EDA?
• Is scaling necessary for PCA in this case? Give justification and perform scaling.
• Comment on the comparison between the covariance and the correlation matrices from this data [on scaled data].
• Check the dataset for outliers before and after scaling. What insight do you derive here? [Please do not treat Outliers unless specifically asked to do so]
• Extract the eigenvalues and eigenvectors.[print both]
• Perform PCA and export the data of the Principal Component (eigenvectors) into a data frame with the original features
• Write down the explicit form of the first PC (in terms of the eigenvectors. Use values with two places of decimals only).
• Consider the cumulative values of the eigenvalues. How does it help you to decide on the optimum number of principal components? What do the eigenvectors indicate?
• Explain the business implication of using the Principal Component Analysis for this case study. How may PCs help in the further analysis?