Summary
Every situation and dataset you see will be unique; however, the problems you encounter with them won't be. In this chapter, you saw issues that will come up repeatedly with the datasets you'll be working with.
We saw how having too much data can be a problem by having highly correlated features, and how you can find that correlation and remove it. We used the example of college recruiting points and rank, but you can easily find others in the real world, such as housing prices – you might have the price per square footage but also have those as separate features.
Working with categorical data is common, but at the end of the day, machine learning models need numbers to be able to work. We saw that there are times when we want to keep relationships between categorical values, such as a rating system, and other times when we don't. We saw how we can use one-hot encoding to encode these categories when we don't want to keep the relationships.
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