Linear Regression in Depth Hands-on | Stats model OLS | Handling Multicollinearity | VIF -Lecture 13

Welcome to this in-depth tutorial on machine learning model building with a focus on Linear Regression! Whether you're a beginner or a seasoned data scientist, this video covers everything you need to know about building, analyzing, and fine-tuning a regression model.

🎯 Topics Covered:
🔹 00:00 - Step-by-step guide to machine learning model building
🔹 00:50 - Real-world example of Linear Regression explained
🔹 11:11 - Understanding Box-Plots and handling outliers effectively
🔹 20:32 - Encoding categorical variables: A key preprocessing step
🔹 29:19 - Assumptions of Linear Regression: What you must check
🔹 31:25 - Handling Multicollinearity and calculating the VIF score
🔹 55:01 - Linear Regression using the OLS method from the Statsmodels library
🔹 64:33 - Testing the assumption of normality of residuals

💡 Why Watch This Video?
Gain practical skills to preprocess data, deal with challenges like multicollinearity, and ensure your model is statistically sound. Perfect for students, professionals, and anyone looking to upskill in data science and machine learning.

📊 Key Takeaways:
🔹Learn how to handle outliers and categorical variables effectively.
🔹Understand crucial regression assumptions and how to test them.
🔹Explore advanced concepts like VIF scores and residual analysis.
🔹Implement Linear Regression using Python's powerful Statsmodels library.

🎓 Meet Your Instructor
Pritam Kudale, an experienced AI educator with 10+ years of expertise, is dedicated to equipping educators with modern AI/ML teaching strategies and tools.

🔗 Connect with Pritam Kudale: LinkedIn Profile   / pritam-kudale-90793236  

🔗 Relevant Links:
Check out more resources in the description below for datasets, code snippets, and additional reading material : https://github.com/pritkudale/ML-for-...

#MachineLearning #LinearRegression #DataScience #AI #PythonProgramming #Statistics