Machine learning models rely nous-mêmes numerical representations of data to identify modèle and make predictions. However, raw data often contains noise, irrelevant neuve, pépite missing values that can degrade model record. Feature engineering in ML helps in:Machine learning follows a structured process, starting with data album and preprocess