Last updated on Aug 1, 2024

How can you remove outliers from a dataset using z-score?

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Outliers are data points that deviate significantly from the rest of the distribution. They can skew the results of your machine learning models and affect their performance. One way to detect and remove outliers from a dataset is using z-score, a measure of how many standard deviations a value is away from the mean. In this article, you will learn how to use z-score to identify and filter out outliers in Python.

Key takeaways from this article
  • Use z-score calculation:
    By computing the z-score, you can identify how far each data point deviates from the mean. Apply this in Python using `scipy.stats.zscore(data)` to spot potential outliers.### *Apply a threshold filter:Set a threshold, such as 3, to determine which z-scores indicate outliers. Use a boolean mask in Python to filter out these extreme values and clean your dataset.
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