Practical Data Science using Python - EDA Project - 2

The video tutorial covers the use of box plots and quantile functions to analyze loan and income data. It explains how to interpret box plots, handle outliers, and perform exploratory data analysis (EDA). The tutorial also discusses univariate analysis of loan amounts and interest rates, highlighting the importance of understanding data distribution and patterns.

What is the importance of exploratory data analysis in understanding data distributions?

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What steps can be taken to remove outliers from the annual income data?

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How can the analysis of loan amounts, funded amounts, and funded amount INV be compared?

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What does the distribution of interest rates reveal about loan disbursement?

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