Find Outliers with Python- 4 Simple Ways

Learn how to use traditional IQR and leverage algorithms to identify anomalies and outliers in your data. In this tutorial, we will be using Isolation Forest, Local Outlier Factor, and Elliptic Envelope to find outliers with just a few lines of code. Ultimately, we will build a function that we can easily apply to other datasets when needed.
Find the notebook here:
github.com/Gae...
Find the dataset here:
github.com/Gae...
#outliers
#python
#dataanalysis
#datascience

[@cgupta63]

I notice you have been active last few days in uploading good videos, specially projects specific. You will soon have a gret numbers in subscribers, view counts, and success. Keep going.

[@absentdata]

Yes, I am definitely trying to build the channel with more content. I hope I'll get new subs. Thanks!!!

[@pramishprakash]

Exellent video sir

[@user-es3wr6uf2l]

Great video thank you!

[@absentdata]

Glad you liked it!

[@mayankprajapati2652]

I tried this code with one of my dataset it gave ValueError: Expected a 1D array, got an array with shape (36, 7) when I executed the traditional_outlier function
how to solve that

[@jeanpierre4370]

It appears that you did it by the column, can you do it by rows?

[@absentdata]

Not sure about your approach. Most database would have a column of data that represent a single variable. Do you mean you would like to identify the row that has an outlier?

[@WhatDoesHappen]

Sir
In this video you just detect the outliers..why didn't you remove them??

[@absentdata]

It depends on your use case. For example, you might want to match those outlier with specific events so in that case they would remain. However, if you have a instrument that was not working properly which created outlier values, then you might want to remove them in that case.