What is the difference between supervised and unsupervised learning?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in  The 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! The difference between supervised and unsupervised learning lies mainly in the type of data used and the learning approach.

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🔹 Supervised Learning

• Definition: A machine learning approach where the model is trained on labeled data (input + correct output).

• Goal: Learn a mapping from inputs (features) to outputs (labels) so the model can predict outcomes for new data.

• Examples of Problems:

  • Classification – Predict categories (e.g., spam vs. not spam).

  • Regression – Predict continuous values (e.g., house prices).

• Examples of Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, Neural Networks.

• Use Cases: Email spam detection, credit scoring, medical diagnosis, sales forecasting.

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🔹 Unsupervised Learning

• Definition: A machine learning approach where the model is trained on unlabeled data (no predefined outputs).

• Goal: Discover hidden patterns, groupings, or structures within the data.

• Examples of Problems:

  • Clustering – Group similar data points (e.g., customer segmentation).

  • Association – Find relationships (e.g., “people who buy bread also buy butter”).

• Examples of Algorithms: K-Means, Hierarchical Clustering, DBSCAN, PCA (Dimensionality Reduction), Apriori.

• Use Cases: Market basket analysis, customer segmentation, anomaly detection, topic modeling.

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