Support Vector Machine (SVM)

A Support Vector Machine, or SVM, is like a smart robot that’s really good at sorting things into two piles. In business intelligence and data analytics, it’s a type of algorithm used for classification. This means that SVM helps to categorize data into different groups. For example, an online retailer could use SVM to figure out which products are likely to be a hit or a miss, by looking at past sales data.

SVM does this by finding the best boundary that separates different categories of data. Imagine drawing a line on the ground to separate cats from dogs. The SVM finds the line that has the widest gap from the nearest cat and the nearest dog, making it clear which side each animal belongs on. In more complex situations, where you can’t separate things with just a straight line, SVM can still figure out the best way to split the data, even if that means bending the line into curves.

This method is popular because it works well even when the data is really complicated or when there are lots of features to consider. For businesses, using SVM can help them predict future trends, make better recommendations to customers, and improve their services. It’s a tool that turns a jumble of data into clear-cut categories, making it easier for companies to see the patterns hidden in their data.