Practical Data Science using Python - EDA Project - 3

The video tutorial covers the analysis of loan status data, focusing on three categories: charged off, fully paid, and current. It demonstrates how to visualize these categories using bar plots and Seaborn's count plot. The tutorial then introduces multivariate analysis, explaining how to use heat maps to identify correlations between data features. Finally, it discusses methods to find null values in datasets, comparing the use of the info function and the ISNA method.

What are the three possible values for loan status mentioned in the text?

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Explain the significance of the charged off status in the context of loan analysis.

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What is the purpose of using a bar plot in the analysis of loan status?

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Describe how multivariate analysis is conducted according to the text.

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What does a heat map represent in the context of analyzing relationships between features?

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What is the relationship between funded amount and installment as discussed in the analysis?

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How can one identify null values in a dataset as mentioned in the text?

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