Unsupervised Learning
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Unsupervised Learning is a type of Machine Learning where the model is trained on unlabeled data.
There are no predefined outputs — the system discovers patterns and structures on its own.
How Unsupervised Learning Works
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Provide raw, unlabeled data
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The algorithm analyzes similarities and differences
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Hidden patterns or groups are identified
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Useful insights are extracted
Types of Unsupervised Learning
1. Clustering
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Groups similar data points together
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Examples:
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Customer segmentation
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Grouping similar images
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2. Association
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Finds relationships between variables
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Example:
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Market basket analysis (items frequently bought together)
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Common Algorithms
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K-Means Clustering
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Hierarchical Clustering
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DBSCAN
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Apriori Algorithm
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Principal Component Analysis (PCA)
Applications
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Customer segmentation
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Anomaly and fraud detection
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Recommendation systems
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Image and document organization
Advantages
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No need for labeled data
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Can discover hidden patterns
Disadvantages
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Results can be harder to interpret
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Lower accuracy compared to supervised learning
Conclusion
Unsupervised Learning is useful when labeled data is unavailable and the goal is to explore data structure and relationships.
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