Mention why feature engineering is important in model building and list out some of the techniques used for feature engineering

Data Science, AI and ML
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#1

Algorithms necessitate features with some specific characteristics to work appropriately. The data is initially in a raw form. You need to extract features from this data before supplying it to the algorithm. This process is called feature engineering. When you have relevant features, the complexity of the algorithms reduces. Then, even if a non-ideal algorithm is used, results come out to be accurate.

Feature engineering primarily has two goals:

  • Prepare the suitable input data set to be compatible with the machine learning algorithm constraints.
  • Enhance the performance of machine learning models.

Some of the techniques used for feature engineering include Imputation, Binning, Outliers Handling, Log transform, grouping operations, One-Hot encoding, Feature split, Scaling, Extracting date.