Regression analysis is a fundamental statistical method used in machine learning to predict continuous outcomes. It's widely applied in fields like finance, healthcare, and engineering. Let's explore its basics!

What is Regression?

Regression models the relationship between a dependent variable and one or more independent variables. For example, predicting house prices based on square footage 🏠.

  • Linear Regression: Simplest form, assumes a linear relationship between variables
  • Polynomial Regression: Fits curves by adding polynomial terms
  • Logistic Regression: Used for classification tasks (though named "regression")
  • Multiple Regression: Involves more than one independent variable

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Applications

Regression is used for:

  • Sales forecasting 📈
  • Risk assessment ⚠️
  • Scientific research 🧪
  • Stock market analysis 📊

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Python Example

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X=[[1], [2], [3]], y=[2, 4, 6])  # Simple linear regression

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Further Reading

For a deeper dive into machine learning concepts, check out our Machine Learning Basics Tutorial.