Regression analysis is a statistical method used to model relationships between variables. It helps predict outcomes based on historical data and is widely applied in fields like economics, machine learning, and social sciences.

🔍 Key Concepts

  • Dependent Variable: The outcome you want to predict (e.g., house prices)
  • Independent Variables: Factors influencing the outcome (e.g., square footage, location)
  • Line of Best Fit: A mathematical model that minimizes errors between predicted and actual values

📈 Types of Regression

  1. Linear Regression
    Models relationships with a straight line: y = mx + b

    Linear_Regression

  2. Logistic Regression
    Used for binary classification problems (e.g., spam detection)

    Logistic_Regression

  3. Polynomial Regression
    Fits curves for non-linear relationships

    Polynomial_Regression

🧠 Applications

  • Predicting stock market trends
  • Estimating customer churn rates
  • Analyzing scientific experiments

For deeper insights into regression types, visit our Regression Types Tutorial.