Practical Data Science using Python - Linear Regression Data Preparation and Analysis 3

The video tutorial covers visual analysis of car datasets, focusing on creating a derived dataset called cars ALR. It explains the use of pair plots to identify linear relationships between features and discusses the importance of identifying and handling multicollinearity in linear regression models. The tutorial also covers feature engineering by converting categorical variables into dummy variables, ensuring they are suitable for regression analysis.

Discuss the importance of retaining only significant variables in a dataset.

What is the significance of using the variance inflation factor (VIF) in feature selection?

How can visualizations like heat maps aid in understanding feature correlations?

What are ordinal categorical variables, and how do they differ from nominal variables?