From the course: Build an AI Application with React and AWS SageMaker
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Feature engineering and transforming data
From the course: Build an AI Application with React and AWS SageMaker
Feature engineering and transforming data
- [Instructor] Data preparation is probably one of the most important step in creating your ML model. So, we'll keep working on our data in Data Wrangler. So, let's work on feature engineering. Just as an introduction, feature engineering in machine learning is the process of using domain knowledge to extract and select features. So, the features are characteristics, properties, or attributes from the raw data that makes the machine learning algorithms work. It is a fundamental step in the data pre-processing phase and can greatly impact the performance of a model. The goal of feature engineering is to provide a set of informative, discriminative, and independent features that allows the learning algorithm to perform optimally. So, we have the data that we imported here, so I'm going to open that data and now you see the data source. If you double click on it, you're going to get back to the screen when you imported that. If you want to show the steps, if you're not seeing the steps…
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