Linear regression is a fundamental statistical and machine learning technique. It is used to model the relationship between a dependent variable and one or more independent variables. This tutorial will guide you through the basics of linear regression.
What is Linear Regression?
Linear regression is a method to predict a continuous target variable based on one or more input variables. The goal is to find the best fit line (or hyperplane in higher dimensions) that minimizes the error between the predicted values and the actual values.
Types of Linear Regression
- Simple Linear Regression: This involves predicting a target variable using only one input variable.
- Multiple Linear Regression: This involves predicting a target variable using more than one input variable.
How does Linear Regression Work?
- Modeling: The relationship between the input variables and the target variable is modeled using a linear equation.
- Training: The model is trained using a dataset containing input variables and their corresponding target values.
- Prediction: Once the model is trained, it can be used to predict the target variable for new data.
Example
Suppose you want to predict the price of a house based on its size. You can use linear regression to find the relationship between the size of the house and its price.
Practice
To practice linear regression, you can use our Python Linear Regression Example.