University students can use unsupervised learning to solve real-life problems. Two main techniques that help with this are clustering and dimensionality reduction. These methods can provide important insights.
Clustering Applications:
Market Segmentation: Companies use clustering to find different groups of customers. For example, K-means clustering looks at spending habits in a group of 1,000 customers to see how they behave.
Anomaly Detection: This helps spot unusual activities, like fraud. By examining patterns in 5 million transactions, it becomes easier to improve security in money transactions.
Dimensionality Reduction:
Data Visualization: Techniques like PCA (Principal Component Analysis) help simplify data. They can change a dataset with 50 features down to just 2 features. This makes it easier to see and understand the data.
Noise Reduction: By getting rid of unnecessary features, the performance of models can get better. In some cases, accuracy can improve by up to 25% in predicting tasks.
University students can use unsupervised learning to solve real-life problems. Two main techniques that help with this are clustering and dimensionality reduction. These methods can provide important insights.
Clustering Applications:
Market Segmentation: Companies use clustering to find different groups of customers. For example, K-means clustering looks at spending habits in a group of 1,000 customers to see how they behave.
Anomaly Detection: This helps spot unusual activities, like fraud. By examining patterns in 5 million transactions, it becomes easier to improve security in money transactions.
Dimensionality Reduction:
Data Visualization: Techniques like PCA (Principal Component Analysis) help simplify data. They can change a dataset with 50 features down to just 2 features. This makes it easier to see and understand the data.
Noise Reduction: By getting rid of unnecessary features, the performance of models can get better. In some cases, accuracy can improve by up to 25% in predicting tasks.