We know that categorical variables contain the label values instead of numeric values. Then, here we handle categorical characteristics by One Hot Encoding, Thus, first, discutiremos One Hot Encoding. There are many techniques for handling categorical variables, some are: The data point value in any categorical characteristic is not in numerical form, but in the form of an object. Such as:-īut here we will only discuss the categorical features, categorical characteristics are those characteristics in which the data type is an object type. The concept of transparency for machine learning models is somewhat complicated, as different models often require different approaches for different types of data. This is the third step in the life cycle of any data science project. Feature engineering is the most important art in machine learning that creates a huge difference between a good model and a bad model.
We can also say that function engineering is the same as applied machine learning. These functions can be used to improve the performance of machine learning algorithms and, if performance increases, will provide the best accuracy. Then, Feature engineering is the process of extracting features from raw data using domain knowledge of the problem.
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