How to use the (unweighted) KNN algorithm for predicting thecarat(numerical) target feature for the following single observation using theEuclidean distancemetric with different number of neighbors:

• cut= good
• color= D
• depth= 60
• price= premium
• (carat= 0.71 but you will pretend that you do not have this information)

Q.2

KNN modeling. Specifically,

1. Perform one-hot encoding of the categorical descriptive features in the input dataset.
2. Scale your descriptive features to be between 0 and 1.
3. Display thelast10 rows after one-hot encoding and scaling.

NOTE:For Parts (B), (C), and (D) below, you arenotallowed to use theKNeighborsRegressor()in Scikit-Learn module, but rather use manual calculations (via either Python or Excel). That is, you will need to show and explain all your solution stepswithoutusing Scikit-Learn. The reason for this restriction is so that you get to learn how some things work behind the scenes.

Q.3

What is the prediction of the 1-KNN algorithm (i.e., k=1 in KNN) for thecarattarget feature using your manual calculations (using the Euclidean distance metric) for the single observation given above?

What is the prediction of the 5-KNN algorithm?

What is the prediction of the 10-KNN algorithm?

This part (E) is an exception to the solution mode instructions for this question. In particular, you will need to use theKNeighborsRegressor()in Scikit-Learn to perform the same predictions in each Part (B) to (D). That is,

• What is the prediction of the 1-KNN algorithm usingKNeighborsRegressor()?
• What is the prediction of the 5-KNN algorithm usingKNeighborsRegressor()?
• What is the prediction of the 10-KNN algorithm usingKNeighborsRegressor()?

Are you able to get the same results as in your manual calculations? Please explain.