Life Expectancy Analysis

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prerequisites
intermediate Python • basics of statistics (regression analysis) • basics of NumPy and pandas
skills learned
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
Monica Guimaraes
1 week · 8-10 hours per week · INTERMEDIATE
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Look inside
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.

project author

Monica Guimaraes
Monica Guimaraes has more than fifteen years of experience organizing and teaching undergraduate courses in automata theory, numerical analysis, programming languages, and statistics. Her passion for artificial intelligence goes back to her college years, having done her thesis in automatic-theorem-proving and working later as an AI researcher. She also has extensive experience in software development in corporations.

prerequisites

This liveProject is for confident Python programmers. To begin this liveProject you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Basics of pandas
  • Basics of NumPy
  • Basic Jupyter Notebook
TECHNIQUES
  • Basics of Python data analysis
  • Regression analysis with statsmodels

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