Data classification techniques are methods used to categorize data into different classes or groups based on specific characteristics. These techniques help organizations manage and analyze large volumes of data efficiently. Common approaches include supervised learning, where models are trained on labeled data, and unsupervised learning, which identifies patterns in unlabeled data. Another important technique is semi-supervised learning, which combines both labeled and unlabeled data to improve classification accuracy. Additionally, decision trees, support vector machines, and neural networks are popular algorithms used in data classification, each offering unique advantages depending on the nature of the data and the classification task.