Normalization is a crucial data preprocessing technique used in machine learning and data analysis. It involves scaling numerical data to a standard range, ensuring that all features contribute equally to the model’s learning process. This is particularly important when dealing with datasets that contain features with varying scales, such as income and age. Without normalization, algorithms may give undue weight to features with larger numerical values, leading to biased results. By applying normalization, we enhance the interpretability and performance of machine learning models, making it a foundational step in building effective predictive systems.