Data transformation is a crucial process in preparing raw data for machine learning models. It involves converting data into a format that is more suitable for analysis, which can enhance model performance and training efficiency. Common techniques include normalization, standardization, resizing images, and data augmentation. Normalization rescales data to a specific range, while standardization centers data around zero with unit variance. These transformations ensure that each feature contributes equally during training, which is vital for model accuracy ².

Additionally, data transformation can address issues like skewed distributions. Techniques such as log transformation, clipping, and scaling can help mitigate biases in algorithms that arise from uneven data distributions. For instance, log transformation is effective for right-skewed data, while reciprocal transformations can be applied to left-skewed data ⁵.

Overall, data transformation is an essential step in feature engineering that facilitates better insights and model performance ¹⁴.