The A to Z of Multicollinearity | Variance Inflation Factor | Data Preprocessing | Data Science

📊 in this video we introduce the concept of multicollinearity and explain why it's a critical aspect of data pre-processing that you need to know.
Data Pre-processing Playlist - tinyurl.com/5c9dakus
PCA reference - • Principal Component An...
Ridge & Lasso reference - • The A to Z of Ridge, L...
We start with the basics, explaining what multicollinearity is and why it's a challenge in statistical modelling. You'll understand how correlated features in your dataset can affect your model's stability and the interpretability of its coefficients. We illustrate this with an example of multiple linear regression, where multicollinearity causes the model to generate multiple equations and increases the variance of coefficients.
We discuss various treatment options to tackle multicollinearity head-on:
1️⃣ Removing Correlated Features: Learn how to identify strongly correlated feature pairs and systematically eliminate one of them to maintain model stability and simplicity.
2️⃣ VIF (Variance Inflation Factor): Understand the VIF and how it quantifies multicollinearity. Discover how to use VIF as a tool to flag high multicollinearity and decide which features to drop.
3️⃣ PCA (Principal Component Analysis): We provide you reference to our detailer tutorials on dimensionality reduction through PCA. Find out how to transform your data into a new feature space to minimize multicollinearity while retaining information.
4️⃣ Ridge and Lasso Regression: Learn how these regression techniques can be your allies in the battle against multicollinearity.
We cover strengths and weaknesses of each approach, helping you choose the right one for your specific problem.
We dedicate a significant portion of the video to explaining the concept of the Variance Inflation Factor (VIF) in depth, ensuring you grasp the intricacies of this vital tool for multicollinearity detection and treatment.
Happy Learning!