#38 Machine Learning & Data Science Challenge 38
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
What do you do if there are outliers?
The following are the approaches to handling the outliers:
Drop the outlier records.
Assign a new value: If an outlier seems to be due to a mistake in your data, you try imputing a value.
If percentage-wise the number of outliers is less, but when we see numbers, there are several, then, in that case, dropping them might cause a loss in insight. We should group them in that case and run our analysis separately on them.
