Practical Data Science using Python - EDA Project - 5

The video tutorial explains how to create categorical features from numeric data, focusing on funded amounts and annual income. It analyzes default ratios across different funded amount ranges and visualizes the relationship between funded amounts and income brackets. The tutorial also examines mean values of numeric features for defaulted and non-defaulted loans, and compares requested loan amounts with disbursed amounts to identify discrepancies. Insights are provided for potential improvements in loan default rates.

What is the significance of the user-defined function mentioned in the text?

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Explain the importance of visualizing the relationship between funded amount and annual income.

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How can the analysis of requested loan amounts versus disbursed amounts reveal issues in the lending process?

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What does the analysis suggest about the qualification criteria for loans based on the findings?

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