Optimizing ML Models: Benefits, Techniques, Challenges, and Best Practices in Data Transformation

Data transformation is a key step in machine learning that converts raw data into a structured format, improving model accuracy and efficiency. Benefits: Enhances predictive performance, accelerates training, and improves interpretability. Techniques: Includes normalization, standardization, encoding categorical variables, handling missing data, and feature engineering. Challenges: Issues like data leakage, high computational cost, and loss of important information can affect model reliability. Best Practices: Careful preprocessing, selecting the right transformations, and preventing overfitting ensure optimal model performance. Effective data transformation leads to cleaner, more meaningful data, enabling machine learning models to deliver better insights and predictions.