Comparison: Supervised vs Unsupervised Learning

Feature	Supervised Learning	Unsupervised Learning
Data Type	Labeled Data	Unlabeled Data
Supervision	Requires supervision	No supervision needed
Input and Output	Input data with output	Only input data
Goal	Map input variable (x) to output variable (y)	Group/categorize data based on patterns
Accuracy	Produces accurate results	May give less accurate results
Number of Classes	Known	Unknown
Use Case	Known output and input	Unknown output, known input
Learning Method	Off-line	Real-time
Computational Complexity	Very complex	Less complex
Problem Types	Regression and Classification	Clustering and Association
Algorithms	Decision Tree, Logistic Regression, Linear Regression, SVM, etc.	KNN, K-Means Clustering, PCA, etc.
Applications	Spam detection, Image classification, Fraud detection, Speech Recognition, Medical Diagnosis	Anomaly Detection, Recommendation Systems, Customer segmentation, Data visualization, Network Analysis