Transformation methods

Transformation methods are techniques used in data analysis and machine learning to modify data into a more suitable form for modeling. They help improve the performance and accuracy of algorithms by scaling features, normalizing distributions, or converting data into different formats. Common methods include scaling data to a specific range, applying mathematical functions to reduce skewness, or encoding categorical variables. Essentially, transformations prepare raw data to be more consistent and meaningful for analysis, making it easier for models to identify patterns and make reliable predictions.