| Supervised Learning | Unsupervised Learning |
|---|---|
| It deals with the labelled data where the output data patterns are also known to the system. | It deals with unlabeled data in which the output is just based on the collection of perceptions. |
| It aims to train the model so that it can predict the output when it is given new data. | It aims to find the hidden patterns and useful insights from the unknown dataset. |
| In this Input and Output data are provided. | In this only input data is provided. |
| It predicts the output. | It finds the hidden patterns in data. |
| It needs supervision to train the model. | It does not need any supervision to train the model. |
| Its Computational Complexity is very complex. | Its Computational Complexity is very less as compared to Supervised Learning. |
| It can also conduct offline analysis. | It employs real-time analysis. |
| It produces an accurate result. | It produces a less accurate result as compared to supervised learning. |
| In this Number of classes are known. | In this Number of classes are not known. |
| It has algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, Multi-class Classification, Decision tree, Bayesian Logic, etc. | It aims to find the hidden patterns and useful insights from the unknown dataset. In this only input data is provided. |
| It can be used for 2 different types of problems i.e., regression and classification. | It can be used for 2 different types of problems i.e., clustering and association. |
| Its application is Spam detection, handwriting detection, pattern recognition, speech recognition etc. | Its application detects fraudulent transactions, data preprocessing etc. |
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