Linear regression is a fundamental statistical and machine learning technique. It is used to model the relationship between a scalar dependent variable and one or more explanatory variables. In this guide, we will delve into the basics of linear regression and its applications.
Key Concepts
- Dependent Variable: The variable we want to predict or explain.
- Explanatory Variables: The variables used to predict the dependent variable.
- Regression Line: The line that best fits the data points and represents the relationship between the variables.
Types of Linear Regression
- Simple Linear Regression: One dependent variable and one explanatory variable.
- Multiple Linear Regression: One dependent variable and multiple explanatory variables.
How Linear Regression Works
- Modeling: We start by fitting a linear model to the data.
- Training: We use historical data to train the model.
- Prediction: We use the trained model to predict new data points.
Applications
- Real Estate: Predicting house prices based on features like size, location, and number of rooms.
- Finance: Predicting stock prices or market trends.
- Healthcare: Predicting patient outcomes based on medical history and test results.
Resources
For more in-depth learning, check out our Machine Learning Models.
Linear Regression