Data Science, Data Analysis, Machine Learning, Statistics, Data Visualization.
classification using techniques such as one-vs-one or one-vs-all. Here’s a simplified process of using SVM for classification: 1. Input Data: — Collect and preprocess the labeled training data. 2. Feature Selection: — Choose the relevant features for the problem. 3. Model Training: — Train the SVM