From the course: Complete Guide to Generative AI for Data Analysis and Data Science

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Feature engineering

Feature engineering

From the course: Complete Guide to Generative AI for Data Analysis and Data Science

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Feature engineering

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- [Presenter] An important step in machine learning model building is feature engineering. Now, when we talk about feature engineering, basically we're talking about modifying a dataset in ways to improve the performance of the model. The idea here is that our dataset may contain relevant information, but it may not be structured in a way that machine learning algorithms can easily pick up on patterns and detect things that are useful in building the model. So with feature engineering, we're trying typically to make things more explicit, make features more explicit that can be useful for building models, or eliminating features that might not really add anything to the model building process and might actually slow it down. So we're modifying the dataset and basically extracting or making explicit information that may be implicit in the model, but not explicit. Now there are different types of feature engineering. There's handling missing values. So for example, you know, some…

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