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Normalization is a common technique used in various fields, particularly in data processing and database management, to organize data efficiently and reduce redundancy. The core principle of normalization is to structure data in a way that minimizes the risk of data anomalies, such as insertion, update, and deletion anomalies, which can occur when data is not properly organized.

In database design, normalization involves a set of rules or stages called normal forms. Each normal form addresses specific types of redundancy and dependencies. The most common normal forms are First Normal Form (1NF), Second Normal Form (2NF), and Third Normal Form (3NF). Achieving higher normal forms generally leads to more robust and maintainable database structures. For example, 1NF requires that all attributes be atomic and that there are no repeating groups. 2NF builds upon 1NF by ensuring that all non-key attributes are fully functionally dependent on the primary key. 3NF further refines this by eliminating transitive dependencies, where a non-key attribute depends on another non-key attribute rather than directly on the primary key.

Beyond databases, normalization also applies to data analysis and machine learning. In these contexts, it refers to the process of scaling numerical data to a common range, typically between 0 and 1 or with a mean of 0 and a standard deviation of 1. This scaling is crucial for algorithms that are sensitive to the magnitude of input features, such as gradient descent-based algorithms and distance-based algorithms like k-nearest neighbors. By normalizing data, the influence of features with larger values is reduced, preventing them from dominating the learning process and ensuring that all features contribute more equally. This process improves the performance and convergence speed of many machine learning models.