In this liveProject, you’ll perform a regression analysis on precleaned data to determine factors relating to American life expectancy. You will learn how to do a comprehensive regression analysis and how to select a model in the presence of multicollinearity. You will use the libraries developed in the second and third projects to check the validity of your final model.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
This liveProject is for confident Python programmers. To begin this liveProject you will need to be familiar with:
- Intermediate Python
- Basics of pandas
- Basics of NumPy
- Basic Jupyter Notebook
- Basics of Python data analysis
- Regression analysis with statsmodels
you will learn
In this liveProject, you’ll learn vital skills for planning and carrying out a thorough regression analysis. These foundational skills will allow you to uncover and understand the nature of complex interactions in your data.
- Detecting multicollinearity with the Variance Inflation Factor
- Doing a comprehensive regression analysis with statsmodels
- Visualizing relationships with seaborn
- Interpreting "QQ-plots," "Residuals vs. Fitted" plots, and "Scale-Location" plots