Imagine you’re about to build an application that lets car owners sell their cars and you want to add a feature that lets allows them evaluate the price of their cars after answering a couple of questions. You begin to ponder different approaches to do this accurately and efficiently. So you ask around from friends and one of them suggests to you.
“Why don’t you use Machine Learning to do this?”
You decide to embrace this challenge and then use Machine Learning to add this new feature to the app. Let the “googling” begin.
In a previous post, we covered an introduction to Machine Learning and some techniques and in this article, we’re going to double-down on Linear Regression and use cases for Linear Regression in our applications.
What is Linear Regression?
Linear regression is a machine learning model that is largely based on statistics and assumes a linear relationship between the input variables and the output variable. In other words by the application of a linear combination if the input variables, we can calculate the output variable which is what we are trying to predict.
Linear Regression is mostly used to generate predictions for continuous variables. It is used to answer questions like
- Predicting the price of a car based on different data points
- Predicting the price of a house based on previous data