Support Vector Machine (SVM) is a supervised learning algorithm that can be used for classification or regression tasks. SVM aims to identify a hyperplane that best separates the data into different classes. The hyperplane is chosen in such a way that it maximizes the margin between the two classes, which in turn reduces the risk of misclassification.
Key Highlights:
- SVM is a powerful classification algorithm that works well with high-dimensional datasets.
- It is particularly useful when the number of features is greater than the number of samples.
- SVM can also handle non-linearly separable data by mapping the data into a higher dimensional space.
References:
- Wikipedia - Support Vector Machine
- Scikit-learn - Support Vector Machines
- Towards Data Science - Support Vector Machines
Applying SVM to Business:
SVM can be applied to a variety of business problems. For instance, it can be used to classify customers into different segments based on their purchase history or demographic information. SVM can also be used to predict whether a customer will churn or not, and to identify the most important features that contribute to customer churn. Additionally, SVM can be used to detect fraud in financial transactions by identifying anomalous patterns in the data. Overall, SVM is a powerful tool that can help businesses make more informed decisions by leveraging the insights hidden in their data.
