In this liveProject, you’ll build your own Python data library to check three of the assumptions of the regression model: normality, linearity, and constant variance. Confidence in your regression models depends on how well you have satisfied these assumptions. Once you’ve developed functions and plots that can check these assumptions, you’ll master techniques for correcting them. With this library, you will expand your data science toolbox with important diagnostics tools that will allow you to be confident in your results.
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 scikit-Learn/statsmodels
you will learn
In this liveProject, you’ll learn vital skills to test the validity of your regression results. These skills are easy to transfer to any regression analysis.
- Checking the assumptions of the regression model
- Programming "QQ-plots," "Residuals vs. Fitted" plots, and "Scale-Location" plots
- Visualizing relationships and distributions with seaborn