Practical Data Science using Python - EDA Project - 6

The video tutorial explores the relationship between loan grades, subgrades, and default ratios at Lending Club. It highlights the use of visualization and the crosstab function to analyze data, revealing insights into high default ratios across certain grades and subgrades. The analysis extends to loan terms, employer impact, and verification status, providing actionable insights for Lending Club to address high default rates. The tutorial also suggests exercises for further analysis, such as examining state-specific default ratios and cyclical influences.

What are the implications of high default ratios for specific loan grades and subgrades?

How can the findings from this analysis help Lending Club improve their loan offerings?

What conclusions can be drawn from the analysis of verification status and default ratios?

What cyclical influences on default ratios were explored in the analysis?