normilization

Normalization is a process used in various fields, primarily in databases and machine learning, to organize data efficiently and effectively. In the context of relational databases, normalization refers to the process of structuring tables to reduce data redundancy and improve data integrity. It involves dividing larger tables into smaller, less redundant tables and defining relationships between them. The primary goal is to eliminate insertion, update, and deletion anomalies, ensuring that data dependencies are properly enforced. There are several normal forms, such as the first normal form (1NF), second normal form (2NF), and third normal form (3NF), each with specific rules for achieving a higher degree of normalization.

In machine learning, normalization, often referred to as feature scaling, is a technique used to standardize the range of independent variables or features of data. This is crucial because algorithms that rely on distance calculations, such as gradient descent or support vector machines, can be sensitive to the scale of input features. By normalizing features, all features contribute more equally to the model's learning process, preventing features with larger values from dominating those with smaller values. Common normalization techniques include min-max scaling, which scales features to a fixed range, typically between 0 and 1, and z-score normalization, which transforms data to have a mean of 0 and a standard deviation of 1. Both database and machine learning normalization aim to prepare data for more robust and accurate processing and analysis.