Linear regression is the simplest ML algorithm — it fits a straight line to data to predict a continuous output value.

How It Works

Finds the line y = mx + b that minimizes the error between predictions and actual values across all training examples.

When to Use It

• Predicting house prices from square footage
• Forecasting sales from advertising spend
• Estimating delivery time from distance

Key Assumptions

• Linear relationship between features and label
• Features are independent of each other
• Errors are normally distributed

Evaluation Metric

Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) — measures average prediction error.

Reference:

Linear regression tutorial

https://developers.google.com/machine-learning/crash-course/linear-regression