Complete Hands-on Outlier Treatment | Multiple Approaches Covered | Data Preprocessing in Python

In continuation to our previous video where we covered in-depth theory involving everything to do with Outliers - right from its definition to detection and treatment, this is a complete hands-on video.
If you haven’t watched the first part, here’s the link:
Outlier Treatment Theory: • The A to Z of dealing ...
Dataset link: www.kaggle.com...
KNN Tutorial Link: • The A to Z of K Neares...
Complete EDA and Data pre-processing playlist - tinyurl.com/5c...
In this video we perform complete hands-on for treating Outliers in Python.
Starting with Outlier Detection Methods:
Two Standard Deviation Rule: We'll show you how to identify outliers using the two standard deviation rule based on the
normal distribution. This is a quick and straightforward method.
Interquartile Range (IQR) Rule: We'll also teach you how to use the IQR rule for detecting outliers, which is more robust
when dealing with skewed data.
Then we cover five different ways of outlier treatment in our hands-on, properly explaining every single line of code.
These five Outlier Treatment Strategies are:
Remove Outliers: Learn how to remove rows containing outliers from your dataset. This approach is useful when you have a strong reason to exclude extreme values and there aren't too many outliers.
Replace Outliers with Median: Discover how to replace outliers with the median value, which is a great way to maintain data integrity while handling extreme values.
Transformation (e.g., Log Transformation): We'll demonstrate the power of transformations, such as log transformation, for dealing with right-skewed data where the outliers might be affecting your analysis.
Winsorization: Understand the concept of winsorization, where you cap and floor the outliers to the nearest permissible values, effectively mitigating their impact.
Convert Outliers to Missing Values and Impute: In cases where you don't want to lose data but need to deal with outliers, we'll show you how to convert outliers to missing values and then use a powerful imputer like the
k-Nearest Neighbors (KNN) imputer from scikit-learn to fill in those gaps.
Not only we show you how to do this, but also explain the pros and cons of each approach.
If you have been looking for a complete tutorial, then you’ve reached the right video.
Happy Learning!

[@rachitmakhija9703]

Thank you for uploading such informative content

[@Neriamonde]

This video is awesome, great content

[@prosmartanalytics]

Thank you! Glad you liked it. 😊

[@JJZ123hdjs]

Nice video, really helpfull, i have a question about the topic:
Would it be valid to apply different methods on a same dataset, example: column1 Z Score transf, column 2 winsor, column 3 Logarithm transf and so on?

[@prosmartanalytics]

Absolutely, we may apply different transformation and treatment approaches for different features.

[@JJZ123hdjs]

@@prosmartanalytics thank you brother!