According to a survey in Forbes, data scientists spend 80% of their time on data preparation. This shows the importance of feature engineering in data science. Here are some valuable quotes about Feature Engineering and its importance:

Coming up with features is difficult, time-consuming, requires expert knowledge. ‘Applied machine learning’ is basically feature engineering — Prof. Andrew Ng.

The features you use influence more than everything else the result. No algorithm alone, to my knowledge, can supplement the information gain given by correct feature engineering — Luca Massaron

What is Feature Engineering?

Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in an improved model accuracy on unseen data.

Basically, all machine learning algorithms use some input data to create outputs. This input data comprises features, which are usually in the form of structured columns. Algorithms require features with some specific characteristic to work properly

Having and engineering good features will allow us to most accurately represent the underlying structure of the data and therefore create the best model. Features can be engineered by decomposing or splitting features, from external data sources, or aggregating or combining features to create new features.

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