🐍 Linear Regression Basics you Need to Know | Model Building and Interpretation Tutorial In Python

Learn how to build a Linear Regression Model, explain it and test regression assumptions using Python programming language. We’ll discuss Ordinary Least Squares Regression (OLS) & the concept of fitting a line to data, intuitively.

Linear regression analysis is used to predict the value of a numeric variable (dependent, y) based on the value of another variable (independent, x), it is one of the most flexible yet powerful statistical tools.

This video is part of a series covering analytics and machine learning in Python where you will learn by doing full projects.

Prerequisites:

Chapters:
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00:00 Introduction to Machine Learning & Linear Regression
04:53 Ordinary Least Squares Regression (OLS)
07:12 Building Linear Regression Model in Python (statsmodels Python module)
08:05 Understanding & Explaining Linear Regression Output in Python
12:09 Testing Linear Regression Assumptions
22:14 Summary
24:01 Impact of Assumption Violation (Linear Regression)
25:26 Predictive Modeling Trials (Linear Regression)
29:52 what’s next?
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Let’s Connect:

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Notes:

Subtitles available for this video
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Soundtrack – Intro:

Track: Max Brhon - Redemption [NCS Release]
Music provided by NoCopyrightSounds.

Track: Egzod, Maestro Chives, Neoni - Royalty [NCS Release]
Music provided by NoCopyrightSounds.
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Soundtrack – Outro:

Track: ATSMXN, XTOM - Enemies [NCS Release]
Music provided by NoCopyrightSounds.

Track: Rex Hooligan x Anna Simone - No More Levitation [NCS Release]
Music provided by NoCopyrightSounds.
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