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

Video Description

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-automations-4579 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 Code: https://ryanandmattdatascience.com/sklearn-multiple-linear-regressions/ 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/ 👨‍💻 Mentorships: https://ryanandmattdatascience.com/mentorship/ 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord: https://discord.com/invite/F7dxbvHUhg 📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT Scikit-Learn and Machine Learning Playlist: https://www.youtube.com/playlist?list=PLcQVY5V2UY4LNmObS0gqNVyNdVfXnHwu8 Simple Linear Regression: https://youtu.be/ukZn2RJb7TU Lasso Regression: https://youtu.be/LmpBt0tenJE Random Forest Regressor: https://youtu.be/YUsx5ZNlYWc Learn how to build a multiple linear regression model in Python using scikit-learn in this comprehensive tutorial. This video covers everything from data preparation to model evaluation using a baseball dataset with multiple features. We start by creating synthetic baseball data with features like hits, doubles, triples, home runs, and strikeouts to predict team wins. You'll learn the key differences between simple and multiple linear regression, where multiple independent variables (features) work together to predict the dependent variable. The tutorial walks through essential steps including data preprocessing, train-test splitting, model fitting, and evaluation using metrics like mean absolute error, mean squared error, and R-squared score. I also explain coefficients and intercepts in the context of multiple features, showing how each variable impacts the final prediction. By the end of this video, you'll understand how to implement multiple linear regression from scratch, interpret model results, and evaluate performance using scikit-learn's built-in methods. Perfect for beginners learning machine learning and data science with Python. Topics covered: multiple linear regression, scikit-learn, pandas data manipulation, train-test split, model evaluation metrics, coefficients analysis, Python machine learning, data visualization with seaborn, and predictive modeling with real-world applications. TIMESTAMPS 00:00 Introduction to Multiple Linear Regression 01:22 Formula and Conceptual Overview 01:37 Setting Up Python Environment 02:09 Creating Sample Data with ChatGPT 05:05 Adjusting Data for Linear Relationships 07:03 Creating the DataFrame 09:10 Visualizing Linear Relationships 10:20 Dropping Unnecessary Columns 12:00 Splitting Data into X and Y 13:24 Train-Test Split 15:27 Importing Linear Regression 16:40 Fitting the Model 18:25 Evaluating Model Performance 20:28 Understanding Y Prediction 21:17 Mean Absolute Error and Mean Squared Error 22:44 Model Coefficients and Intercept 24:12 Final Recap and Conclusion OTHER SOCIALS: Ryan’s LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/ Matt’s LinkedIn: https://www.linkedin.com/in/matt-payne-ceo/ Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.