You work as a machine learning specialist at a firm that runs a web application that allows users to research and compare real estate properties worldwide. You are working on a property foreclosure model to predict potential price drops. You have decided to use the SageMaker Linear Learner algorithm. Here is a small sample of the data you’llhave to work with:
| Type | Bedrooms | Area | Solar_Rating | Price | Foreclosed |
| condo |2| 2549 | H | 125400| N|
| house |4| 4124 | M | 250250| Y|
| house |3| 3250 | | 200000| N|
| condo |1| 900 | N |90250 | N|
| condo |2|?| L| 125400| Y|
In order to feed this data into your model, you will first need to clean and format your data.
Which of the following SageMaker built-in scikit-learn library transformers would you use to clean and format your data? Select 4. 

Answer options:

A.StandardScaler to encode the Solar_Rating feature
B.OneHotEncoder to encode the Area feature
C.SimpleImputer to complete the missing values in the Solar_Rating and Area features
D.OneHotEncoder to encode the Type feature
E.OrdinalEncoder to complete the missing values in the Solar_Rating and Area features
F.OrdinalEncoder to encode the Solar_Rating feature
G.LabelBinarizer to encode the Foreclosed feature
H.
MinMaxScaler to encode the Foreclosed feature

Answers: C, D, F, and G
Options A is incorrect. From the scikit-learn API Reference, the StandardScaler transformer is used to Standardize features by removing the mean and scaling to unit variance. The OrdinalEncoder transformer would be the better choice for this feature since H > M > L > N. Therefore, this feature has ordinal values.
Option B is incorrect. The OneHotEncoder transforms nominal categorical features and creates new binary columns for each observation. The Area feature holds numerical or quantitative data, which does not need to be transformed.
Option C is correct. The Solar_Rating and Area features have missing data in some observations. From the scikit-learn API Reference: the SimpleImputer transformer is used to complete missing values.
Option D is correct. The Type feature is a good candidate for the OneHotEncoder transformer since the Type feature holds a limited number of categorical types. The OneHotEncoder transforms nominal categorical features and creates new binary columns for each observation.
Option E is incorrect. From the scikit-learn API Reference: the OrdinalEncoder transformer encodes categorical features as an integer array. This encoder does not complete missing values.
Option F is correct. From the scikit-learn API Reference: the OrdinalEncoder transformer encodes categorical features as an integer array that maintains the ordinal nature of the data. Since H > M > L > N, this feature has ordinal values.
Option G is correct. The Foreclosed feature holds one of two choices, either a ‘Y’ or ‘N’. Therefore, this feature is a good candidate for the LabelBinarizer. From the scikit-learn API Reference: the LabelBinarizer transformer binarizes label in a one-versus-all fashion.
Option H is incorrect. From the scikit-learn API Reference: the MinMaxScaler transformer transforms features by scaling each feature to a given range. The Foreclosed feature has binary data: either ‘Y’ or ‘N’. So it is better suited to the LabelBinarizer transformer.
Reference:
Please see the Amazon SageMaker developer guide titled Use Scikit-learn with Amazon SageMaker, and the scikit-learn API Reference.