How to Handle Outliers in Data | Identify & Remove Outliers Using Python

Here’s what we’ll cover:
- What are outliers?
An explanation of outliers and their significance in data.
- Why it’s important to identify outliers in analytics and machine learning.
- Methods to identify outliers using Python**: Z-Score method, IQR (Interquartile Range), and visualization techniques.
- Step-by-step Python implementation to detect and handle outliers in your data using libraries like Pandas, NumPy, SciPy, and Matplotlib.

By the end of this video, you’ll understand how to:
- Detect outliers effectively.
- Make informed decisions on whether to remove, cap, or analyze outliers separately.
- Implement outlier handling techniques using Python.

This video is perfect for data scientists, analysts, or anyone working with datasets in machine learning, statistics, or data analytics. Don’t forget to like and subscribe for more data science content!

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