Learn which machine learning approach – clustering, regression, or classification – is best for predicting future numerical values like the number of gift cards that will be sold. Explore how regression models can forecast sales and other quantities.
Table of Contents
Question
You have to predict the number of gift cards that will be sold next year. Which type of machine learning should you use?
A. clustering.
B. regression.
C. classification.
Answer
B. regression.
Explanation
To predict the number of gift cards that will be sold next year, you should use regression. Here’s why:
Regression is a type of supervised machine learning used to predict numerical values. It works by finding patterns and relationships between input features and a continuous output variable, like sales numbers or prices.
The goal is to train a model on historical data so it can predict future values of the output variable when given new input data. Some common regression algorithms include linear regression, polynomial regression, and decision trees.
In contrast, the other machine learning types are not well-suited for this task:
- Clustering is an unsupervised learning method that groups similar data points together. It does not predict values.
- Classification is supervised learning, but is used to predict categorical outputs (e.g. “high sales” vs “low sales”), not numerical values.
Since your task is to forecast a specific number – how many gift cards will be sold – regression is the appropriate choice. You would train a regression model using historical sales data and related features, then use the trained model to predict next year’s gift card sales.
In summary, regression is the go-to machine learning approach for predicting future numerical values, making it ideal for forecasting sales figures like the number of gift cards that will be purchased.
Regression is used for predicting numerical values, such as the number of gift cards that will be sold next year. It helps estimate continuous variables based on historical data.
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