Applications of unsupervised machine learning for business

Unsupervised learning refers to the training of an AI system using information that is not classified or labeled. It allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabelled data.

The output is dependent upon the coded algorithms.

The unsupervised learning algorithm can be further categorized into two types of problems:

• Clustering
• Association

Applications of unsupervised machine learning for business

• Data Mining such as audience segmentation , Customer persona investigation.
• Anomaly detection can discover unusual data points in your datasets. It is useful for finding fraudulent transactions.
• Singular value decomposition (SVD) is used to extract certain types of information from the datasets such as take out info on every user located in Tampa, Florida.
• Clustering automatically split the datasets into groups base on their similarities.

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