Linear regression is a fundamental concept in machine learning. It is used to predict a continuous value based on input features. This crash course will give you a basic understanding of linear regression, its types, and how it works.

Key Concepts

  • Regression: A supervised learning algorithm that predicts a continuous value.
  • Linear: The relationship between the input features and the output is linear.
  • Regression Line: The line that best fits the data points.

Types of Linear Regression

  1. Simple Linear Regression: One input feature and one output feature.
  2. Multiple Linear Regression: Multiple input features and one output feature.

How Linear Regression Works

  1. Data Preparation: Collect and preprocess the data.
  2. Model Training: Use the data to train the model.
  3. Prediction: Use the trained model to predict new values.

Example

Suppose you want to predict the price of a house based on its size.

  • Input Feature: Size of the house
  • Output Feature: Price of the house

You can use linear regression to find the relationship between the size of the house and its price.

More Information

For a deeper understanding of linear regression, you can read our detailed guide on Linear Regression.

Linear regression is a powerful tool for predicting continuous values. By understanding its concepts and working, you can apply it to various real-world problems.