Please answer the following questions within the Google Colab file.

Note: There are 12 total questions to be answered.

I will send a secure link to the tutor for the Google Colab file.

***These are the 12 questions to be answered within the Google Colab file:

1: Explain the reasons behind Dimension Reduction?

2: Define multicollinearity?

3: Define PCA.

4: What is the difference between PCA and LDA?

5: Is it necessary to standardize the data?

6. How correlation is used for dimension reduction?

7. Do we always need to split our dataset into train and test? Should we do it when we want to perform PCA?

8. If we use PC1 what percentage of the dataset can we explain?

9. Is there a situation that the correlation between PC1 and PC2 is more than 0.5?

10. Can we use PCA for classification? Explain!

11. What are the three cases of regression response variables discussed.

12. What kind of regression should we use when trying to predict a count response variable?