This story was originally published on HackerNoon at: https://hackernoon.com/feature-engineering-for-machine-learning. Discover how feature engineering enhances ML models. Learn effective techniques for creating and processing features to maximize and process features. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #feature-engineering, #ml-models, #feature-engineering-techniques, #predictive-modeling, #ml-model-training-data, #ml-model-performance, #data-preprocessing, #hackernoon-top-story, and more. This story was written by: @sumitmakashir. Learn more about this writer by checking @sumitmakashir's about page, and for more stories, please visit hackernoon.com. Feature engineering is crucial for maximizing the performance of machine learning models. By creating and processing meaningful features, even simple algorithms can achieve superior results. Key techniques include aggregation, differences and ratios, age encoding, indicator encoding, one-hot encoding, and target encoding. Effective feature processing involves outlier treatment, handling missing values, scaling, dimensionality reduction, and transforming targets to normal distribution.