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Question 164 - MLS-C01 discussion

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A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.

Which model will meet the business requirement?

A.
Logistic regression
Answers
A.
Logistic regression
B.
Linear regression
Answers
B.
Linear regression
C.
K-means
Answers
C.
K-means
D.
Principal component analysis (PCA)
Answers
D.
Principal component analysis (PCA)
Suggested answer: B

Explanation:

The best model for predicting housing prices based on a historical dataset with 32 features is linear regression. Linear regression is a supervised learning algorithm that fits a linear relationship between a dependent variable (housing price) and one or more independent variables (features). Linear regression can handle multiple features and output a continuous value for the housing price. Linear regression can also return the coefficients of the features, which indicate how each feature affects the housing price. Linear regression is suitable for this problem because the outcome of interest is numerical and continuous, and the model needs to capture the linear relationship between the features and the outcome.

References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Regression vs Classification in Machine Learning

AWS Machine Learning Training - Linear Regression with Amazon SageMaker

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asked 16/09/2024
Omar Olaya
38 questions
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