Linear regression

That is a very simple ML algorithm, linear regression. In technical terms, linear regression attempts to represent the relationship between one or more independent variables (points on X-axis) and a numeric outcome, or dependent variable (value on Y-axis), by fitting the equation of a line to the data. For example, you might want to relate the weights (Y) of individuals to their heights (X) using linear regression. Linear regression models are widely used because of their simplicity.

Business use cases:

Predictive analytics: demand and sales volume analysis e.g., number of items that a customer is likely to purchase

Operation efficiency: optimize business processes, e.g., understand the impact of oven temperature on the shelf life of cookies or predict traffic jams

Supporting decisions: aid business management, e.g., given data on finances, operations, and customer purchases; make data-driven decisions instead of following your intuition alone; understand product-sales drivers such as competitive prices, distribution, advertisement, etc

New insights: Over time businesses gather a large volume of unorganized data that has the potential to yield valuable new insights, e.g., analysis of data from point of sales systems and purchase accounts may highlight unexpected market patterns