Question

You have a dataset of customers that includes each customer's gender, location, and other personal attributes. The label you are trying to predict is how much sales revenue each customer is likely to generate for the business based on these attributes. What type of machine learning outcome is this problem suited for?

1) Dimensionality reduction
2) Regression
3) Clustering
4) Classification

Answer 1

The machine learning outcome best suited for predicting the sales revenue based on customer attributes is regression. Regression models predict continuous outcomes and are ideal for forecasting numerical values like revenue.

Step-by-step explanation:

The type of machine learning outcome that is suited for predicting how much sales revenue each customer is likely to generate based on their gender, location, and other personal attributes is regression. Regression is used in machine learning to predict a continuous outcome, such as sales revenue, from a given set of predictors. The given dataset, with attributes like gender and location influencing sales, is a classic example where regression can be applied to forecast a customer's revenue generation. Unlike classification, which is about predicting a label, or clustering which is for finding similar groups in the data, regression deals directly with the prediction of quantities. Using a regression model similar to the one provided in the example, where sales growth is predicted using the model ‘ŵ = 101.32 + 2.48x’, we can insert the days as 'x' to get revenue predictions for different days.

To predict the sales on day 60, you simply substitute x with 60 in the model equation to get ‘ŵ = 101.32 + (2.48 × 60)’, and for day 90, you use ‘ŵ = 101.32 + (2.48 × 90)’.