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AI-900: What Features Should You Use to Predict Customer Income Range with Azure Machine Learning?

Learn how to select the right features like age and education level to accurately predict a customer’s income range using Azure Machine Learning. Improve your model’s performance now.

Table of Contents

Question

You have a customer dataset, and need to predict the income range of a given customer.

First Name Last Name Age Educational Level Income Range
Orlando Gee 45 University 25000 – 50000
Keith Harris 36 High school 25000 – 50000
Donna Carreras 52 University 50000 – 75000
Janet Gates 21 University 75000 – 100000

Which two fields should you use as features?

A. Last Name
B. Age
C. First Name
D. Educational Level
E. Income Range

Answer

To predict a customer’s income range using Azure Machine Learning, you should use the customer’s Age and Educational Level as the input features.

B. Age
D. Educational Level

Explanation

Age and education level are likely to be predictive of income range. In general, income tends to increase with age up to a certain point as people gain work experience and advance in their careers. Higher levels of education also correlate with higher income on average.

In contrast, first and last name are unlikely to have any meaningful relationship with income. Names are essentially arbitrary labels.

Income range, the value you are trying to predict, cannot be used as an input feature. The goal is to predict the income range based on the other data you have about the customer. Using income range as an input would be like giving your model the answer!

So in summary, age and educational level are the best choices since they capture meaningful information related to income, without being arbitrary or giving away the value you want to predict. Using these relevant features will allow you to train an effective machine learning model to predict customer income ranges.

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