Meaning of Linear Regression (Machine Learning)

Simple definition

Linear regression in machine learning is an algorithm used to predict numerical values by learning a linear relationship between input features and a target variable.

How to use Linear Regression (Machine Learning) in a professional context

It is commonly applied in predictive analytics, such as forecasting demand, predicting house prices, or assessing risks in finance.

Concrete example of Linear Regression (Machine Learning)

A data scientist trains a linear regression model to predict house prices using features like square footage, number of bedrooms, and location.

Q1: Is linear regression suitable for large datasets?

A1: Yes, it performs well with large datasets if the data has a linear relationship.

Q2: Can linear regression handle categorical variables?

A2: Yes, but categorical variables must first be encoded into numeric form (e.g., one-hot encoding).

Q3: How does it differ from other ML algorithms?

A3: Linear regression is simpler and interpretable, but less effective for non-linear or complex relationships.
Related Blog articles
Our Data Analytics bootcamp in Tokyo is now eligible for the HelloWork subsidy!

Our Data Analytics bootcamp in Tokyo is now eligible for the HelloWork subsidy!

If you’ve been thinking about leveling up your career with data skills, there’s never been...

Our favorite Tokyo tech communities and meetups in 2026

Our favorite Tokyo tech communities and meetups in 2026

Since launching our tech community events at Le Wagon Tokyo back in 2017, we’ve hosted...

Meet our students: using Data Science and English to unlock an international career

Meet our students: using Data Science and English to unlock an international career

Meet Jin, going through our part-time Data Science & AI bootcamp while working at the...

Suscribe to our newsletter

Receive a monthly newsletter with personalized tech tips.