Classification Analysis

Classification analysis is a supervised machine learning technique used to categorize data into predefined classes or labels based on input features. It involves training a model on labeled data to learn patterns and relationships, then applying it to new, unseen data to predict class membership. Common applications include spam detection, medical diagnosis, and customer segmentation.

Also known as: Classification, Classification Modeling, Class Prediction, Categorization Analysis, Supervised Classification

Why learn Classification Analysis?

Developers should learn classification analysis when building predictive systems that require categorical outcomes, such as fraud detection in finance or sentiment analysis in natural language processing. It is essential for tasks where data needs to be organized into discrete groups, enabling automated decision-making and insights from structured or unstructured datasets.

Learning Resources

• docs: Scikit-learn Classification Documentation
• course: Coursera Machine Learning Course by Andrew Ng
• tutorial: Towards Data Science: Classification in Machine Learning
• tutorial: Kaggle Classification Tutorial