Thread by @Sachintukumar

Day 42 of #100dayswithMachinelearning

Topic -- Outlier Detection & Removal using Z-score Method

A Thread 🧵

The Z-score method is statistical approach used for detecting & removing outlier in dataset. An outlier is observation that lies far away from other observation in dataset. Such observations can significantly affect statistical properties of dataset & lead to erroneous conclusion

Approach for Outliers

- The very first step will be setting the upper and lower limit

- The first technique for dealing with outliers is trimming & this is regardless of what kind of data distribution you are working with, trimming is an applicable and proven technique for most

Capping is another technique for dealing with bad data points; it is useful when we have many outliers, and removing a good amount of data from the dataset is not good

Limitations of Z-Score

we mean that it only works with the data which is completely or close to normally distributed, which in turn stimulates that this method is not for skewed data, either left skew or right skew. For the other data, we have something known as (IQR) method

It is important to note that outlier removal can significantly affect the statistical properties of the dataset, and should be done with caution.

Blog by @AnalyticsVidhya

analyticsvidhya.com/blog/2022/08/d…

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