Mastering Multiple Linear Regression in Scikit-Learn: A Step-by-Step Guide

Welcome to our comprehensive guide on Multiple Linear Regression! 📈 In this tutorial, we'll dive deep into the world of statistical analysis and predictive modeling using multiple linear regression. Whether you're a beginner looking to learn the fundamentals or an experienced data scientist aiming to refine your skills, this video has something for everyone
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As a full-time data analyst/scientist at a fintech company specializing in combating fraud within underwriting and risk, I've transitioned from my background in Electrical Engineering to pursue my true passion: data. In this dynamic field, I've discovered a profound interest in leveraging data analytics to address complex challenges in the financial sector.
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[@xyzcrazy4231]

dude, this is awesome!! i've been learning machine learning to pursue a data analytics career and I've been taking some classes online, but none have gone too in depth when it comes to multiple independent variables. your coding is clean and your instructions are so thorough and precise! thanks so much for making this video!

[@davidissah1466]

hey, man. i am from nigeria, africa. i am still in my learning phase of data science. no job, yet :)

[@harshitsahu5849]

Hey man, quality lectures. Love them. It'd great if you could rearrange the playlist a bit so as to provide a better flow and sequence of lectures.

[@ShiftKoncepts]

Hey how would you do it if the data was non linear? Meaning you’d have to use a polynomial with degree?

[@A-K-I-R-A-]

Your tutorials are very good, thank you!

[@OrangeTomato474]

Does multiple regression work when you are trying to explain a factor using other factors rather than predicting??

[@migamafiri6473]

High display quality of the 'code environment V

[@andyhong3003]

Another great video! In following your tutorial exactly: from sklearn.linear_model import LinearRegression; lr = LinearRegression(), and then lr.fit(X_train, y_train). I am getting a "name 'ir' is not defined" error. Do you know a quick why? Thanks!