Liner Regression

Linear regression is a supervised learning algorithm used for predicting a continuous outcome variable (also known as the dependent variable) based on one or more predictor variables (also known as independent variables). The goal of linear regression is to find the best-fitting line that can approximate the relationship between the dependent variable and the independent variables.

Here is an example of linear regression implemented in Python using the scikit-learn library:

This code trains a linear regression model on the input data X and y and uses it to predict the outcome for a new set of input data X_test. The prediction can be obtained using the predict method of the model. In this example, the prediction for X_test is [10].

The scikit-learn library provides a convenient and easy-to-use implementation of linear regression, which can handle a wide range of regression problems with various types of predictor variables and different types of relationships between the dependent and independent variables.