Question: 4) Principal Component Analysis 1. Project the data onto the top 2 selected principal components to obtain a lower-dimensional representation. 5) Interpretation 1. Interpret the

4) Principal Component Analysis 1. Project the data onto the top 2 selected principal components to obtain a lower-dimensional representation. 5) Interpretation 1. Interpret the fraction of data variance explained by your projected data. 6) Reconstruction 1. Reconstruct the data from the low-dimensional data. 2. Compare the reconstructed data with the original data, comment on the quality of reconstruction. 7) Principal component regression 1. Build a principal component regression model between Y and the 2 principal components 2. Use the model to make a prediction of product cost at X1=12.8, X2=8.6, X3=1.53

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