What is the difference between supervised and unsupervised learning?

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The key difference between supervised and unsupervised learning lies in whether the algorithm is trained on labeled data.

1. Supervised Learning:
   In supervised learning, the algorithm is trained on a labeled dataset, meaning each input has a corresponding output (or label). The goal is to learn a mapping from inputs to outputs so the model can predict the correct label for new, unseen data.

   • Examples:

     • Classification (e.g., spam detection, image recognition)

     • Regression (e.g., predicting house prices)

   • Common algorithms: Linear regression, logistic regression, decision trees, support vector machines, neural networks.

   • Use cases: Email spam detection, fraud detection, customer churn prediction.

2. Unsupervised Learning:
   In unsupervised learning, the data has no labels. The algorithm tries to find patterns, structures, or groupings within the data on its own. The focus is on understanding the underlying structure or distribution.

   • Examples:

     • Clustering (e.g., grouping customers by behavior)

     • Dimensionality reduction (e.g., simplifying data for visualization or preprocessing)

   • Common algorithms: K-means clustering, hierarchical clustering, PCA (Principal Component Analysis), DBSCAN.

   • Use cases: Market segmentation, anomaly detection, recommendation systems.

Summary:

• Supervised learning learns from labeled data to make predictions.

• Unsupervised learning finds hidden patterns in unlabeled data.
  Both are core techniques in machine learning, used depending on the type and goal of the data analysis.

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