Remove Outliers Using IQR | Boost Accuracy in Machine Learning

Описание: Welcome to ML Journey: Day by Day
Where we master one machine learning concept every single day!

📂 In This Video:
We’ll focus on Outlier Removal using the IQR Method in Machine Learning.
Outliers can silently destroy your model’s accuracy — pulling regression lines away, biasing predictions, and making your ML pipeline unreliable.

With the IQR method (Interquartile Range), you’ll learn how to detect and handle outliers in Python to boost model performance.

🧠 What You’ll Learn in This Video:

What the IQR method is and how it works

How to detect outliers using IQR in Python

Trimming & Capping techniques with IQR

⏳ Timestamps:

00:00 - Introduction
00:34 - Explanation of IQR method
01:35 - Boxplot Explanation
03:01 - Code Walkthrough
05:10 - Detecting outliers using IQR
06:16 - Trimming with IQR
06:55 - Capping with IQR
07:44 - Wrap-Up

This skill is essential for data preprocessing and will make your machine learning models more accurate and reliable in both regression and classification tasks.

📂 This video is part of my playlist:
👉 ML Journey: Day by Day

🔗 Source Code on GitHub:
https://github.com/WasayRabbani/Machi...

✅ Don’t forget to:
👍 Like if this helped
📩 Comment your questions — I reply to everyone!
🔔 Subscribe for daily ML insights


#Outliers #IQR #MachineLearning #DataPreprocessing #PythonML #MLTutorial #FeatureEngineering #LearnMachineLearning #PandasTutorial #ScikitLearn #AI #MLJourney #DataScienceProjects