Feature Engineering in Machine Learning Projects Principal Component Analysis (PCA)
Feature Engineering in machine learning is the process of transforming raw data into features that are more informative and useful for machine learning algorithms. What is Feature Engineering? Feature engineering is the pre-processing step of machine learning, which extracts features from raw data . It helps to represent an underlying problem to predictive models in a better way, which as a result, improve the accuracy of the model for unseen data. The predictive model contains predictor variables and an outcome variable, and while the feature engineering process selects the most useful predictor variables for the model. Why is it important? Improved Model Performance: Well-engineered features can significantly enhance the accuracy, speed, and robustness of machine learning models. Better Interpretability: Good features can make the model's predictions more interpretable and easier to understand. Reduced Dimensionality: Feature e...
