How can you identify missing values in a dataset for ML models?
Missing values are a common problem in data science, especially when you want to use a dataset for machine learning (ML) models. Missing values can affect the quality, accuracy, and performance of your ML models, so it is important to identify and handle them properly. In this article, you will learn how to identify missing values in a dataset for ML models using Python and some popular libraries.
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Use pandas methods:Pandas, a Python library, offers functions like `isnull()` or `isna()` to flag missing data in your dataset. This makes it simple to pinpoint where you need to focus your cleaning efforts.
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Visualize missing data:Creating a heatmap can help you see the spread of missing values at a glance. It's not just about finding gaps; it's about understanding their patterns so you can make smarter decisions on how to handle them.