Data transformation in ML: Benefits, Methods, Challenges, and Practices

Data transformation in machine learning improves model accuracy by converting raw data into a structured format. Benefits include better performance, faster training, and enhanced interpretability. Methods involve normalization, standardization, encoding categorical data, handling missing values, and feature engineering. Challenges include data leakage, computational cost, and loss of information. Following best practices, such as careful preprocessing, selecting the right transformation techniques, and avoiding overfitting, ensures optimal model efficiency and reliability.