Practical Data Science using Python - Linear Regression Model Evaluation and Optimization

The video tutorial covers feature selection techniques for optimizing linear regression models. It begins with dropping features based on probability values, then checks multicollinearity using Variance Inflation Factor (VIF). Recursive Feature Elimination (RFE) is introduced for automatic feature selection. The tutorial concludes with model testing, validation, and performance evaluation, achieving an R-squared score of 86% for the test data.

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