Hands-on Multicollinearity Treatment | Variance Inflation Factor | Data Preprocessing in Python

Welcome to the next instalment of our Data Pre-processing series! In this practical, hands-on tutorial, we perform hands-on Multicollinearity treatment. If you missed our previous video covering theory and different approaches, you may refer the links provided.

Dataset Link - https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic

Complete Data Pre-processing Playlist - https://tinyurl.com/5c9dakus
Multicollinearity theory - https://youtu.be/8YC73paDntY
PCA Hands-on - https://youtu.be/m7sWi-lCUUU
Ridge and Lasso Hands-on - https://youtu.be/87V3JsvYVUU

In this video, we kick things off by introducing a real-world dataset that boasts 30+ features. But here's the twist: many of these features exhibit strong correlations, which can trouble our predictive models.

To address this issue effectively, we explore powerful Multicollinearity treatment approaches:

1) Feature Pruning based on Pearson's Correlation Coefficient:
We start by identifying pairs of highly correlated features using Pearson's correlation coefficient threshold. When two features are closely related, we make an informed decision to drop one of them, ensuring our dataset remains lean and mean. This method optimizes model performance and interpretability.

2) Recursive Feature Elimination with Variance Inflation Factor (VIF):
The second approach is a more advanced technique. We demonstrate how to apply VIF smartly using a while loop. This iterative process allows us to systematically eliminate features with high VIF, effectively mitigating multicollinearity. By doing so, we improve the stability and reliability of our models.

Finally, we compare the two approaches, highlighting the advantages and potential trade-offs of each. Understanding when to use Pearson's correlation threshold and when to employ VIF-based recursive elimination is crucial for tackling multicollinearity.

Happy Learning!