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

Data transformation is a crucial step in machine learning that converts raw data into a structured format, improving model accuracy and efficiency. Benefits: Enhances model performance, speeds up training, and ensures better interpretability. Methods: Includes normalization, standardization, handling missing values, encoding categorical data, and feature engineering. Challenges: Data leakage, high computational costs, and potential information loss can impact model effectiveness. Best Practices: Proper preprocessing, choosing the right transformation techniques, and preventing overfitting help create reliable ML models. Effective data transformation ensures cleaner, more meaningful data, leading to better predictions and insights.