Feature Engineering is one of the most important steps to complete before starting a Machine Learning analysis.Most of the basic Feature Engineering techniques consist of finding inconsistencies in the data and of creating new features by combining/diving existing ones. Creating the best possible Machine Learning/Deep Learning model can certainly help to achieve good results, but choosing the right features in the right format to feed in a model can by far boost performances leading to the following benefits:

• Enable us to achieve good model performances using simpler Machine Learning models.
• Using simpler Machine Learning models, increases the transparency of our model, therefore making easier for us to understand how is making its predictions.
• Reduced need to use Ensemble Learning techniques.
• Reduced need to perform Hyperparameters Optimization.

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