Why is feature engineering important in data science?
Answer (1)
Answer:Feature engineering is important in data science because it transforms raw data into meaningful inputs that improve the performance and accuracy of machine learning models. By creating, selecting, or modifying features based on domain knowledge and data patterns, data scientists can help models better understand relationships within the data. Good feature engineering can reveal hidden trends, reduce noise, and enhance model interpretability. In many cases, the quality of features has a greater impact on a model's success than the choice of algorithm itself. Ultimately, feature engineering is essential for turning data into insights that drive better decisions.
