Linear Regression

As such, linear regression was developed in the field of statistics and is studied as a model for understanding the relationship between input and output numerical variables, but has been borrowed bymachine learning. It is both a statistical algorithm and a machine learning algorithm

Regression Correlation vs Casuality

Multiple regression, like all statistical techniques based on correlation, has a severe limitation due to the fact that correlation doesn't prove causation.

Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. However this can lead to illusions or false relationships, so caution is advisable;[2] for example, correlation does not imply causation.

https://www.ma.utexas.edu/users/mks/statmistakes/causality.html

Age -> Shoe size
Age -> Reading score
Shoe size and reading score correlated

Not casuality variable, but can be actionable variable

For marketing, we can't find age due to privacy. But we can use shoe size to find kids with higher reading score
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Conversion rate study

Search Engine vs Recommendation Engine

Recommendation engine is a search engine without user intent.

With a search engine, customers find products through an active search, assuming customers know what they want and how to describe it when forming their search query.

Recommendation engines proactively identify products that have a high probabalility of being something the consumer might want to buy. Each time customers