🔍 Unsupervised Learning

📘 Definition: The model finds patterns or structure in unlabeled data.

🎯 Goal: Group or reduce data without prior labeling.

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🧩 Analogy

Think of it like sorting puzzle pieces without seeing the final picture. You group by shape, color, or edge type — without knowing exactly what you’re forming.

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📘 Official Definition

Unsupervised Learning is a type of machine learning that looks for previously undetected patterns in a dataset without pre-existing labels and with minimal human supervision.

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🧠 Simple Explanation

Unsupervised Learning is like exploring without a guide.

Imagine you walk into a new city without a map or labels. You explore and start noticing patterns — tall buildings in one area, parks in another. You naturally group things based on similarities.

In unsupervised learning, the machine is given data without labels and tries to find patterns or groupings on its own.

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✅ Real-World Examples

• Customer Segmentation: Grouping customers based on buying behavior.
• Topic Modeling: Automatically grouping articles by topic.
• Anomaly Detection: Identifying unusual patterns in credit card transactions.
• Market basket analysis: Finds items that are frequently bought together in transactions.

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🔍 Popular Algorithms:

• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• DBSCAN

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Sources:

• IBM Cloud Learn - Unsupervised Learning
• Wikipedia - Unsupervised Learning
• Scikit-learn Documentation