What's the difference between classification and regression?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in hThe primary goal of a data science project is to extract actionable insights from data to support better decision-making, predictions, or automation—ultimately solving a specific business or real-world problem.

Great question! Both classification and regression are types of supervised machine learning, but they solve different kinds of problems:

• Classification → The goal is to predict a category or class label. The output is discrete, meaning the model assigns data points to predefined groups.

  • Example: Predicting whether an email is spam or not spam, or whether a patient has diabetes or no diabetes.

  • Algorithms: Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, Neural Networks.

• Regression → The goal is to predict a continuous numerical value. The output is not a class but a real number.

  • Example: Predicting the price of a house, temperature tomorrow, or sales revenue for next month.

  • Algorithms: Linear Regression, Polynomial Regression, Support Vector Regression, Gradient Boosting Regressors.

Key Difference:

• Classification answers the question: “Which group does this belong to?”

• Regression answers the question: “What value should we predict?”

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