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 discussed in the video?

• Defaulted, Settled, Active

• Open, Closed, In progress

• Pending, Approved, Rejected

• Charged off, Fully paid, Current

Which library is used to create bar plots for visualizing loan status?

• Plotly

• Matplotlib

• Seaborn

• Pandas

What percentage of borrowers have a 'charged off' status according to the video?

• 14.2%

• 25.1%

• 10.5%

• 20.3%

What is the primary purpose of using a heat map in multivariate analysis?

• To calculate the mean of a dataset

• To identify correlations between multiple features

• To display the distribution of a single variable

• To visualize time series data

In a heat map, what does a darker shade typically indicate?

• Random correlation

• No correlation

• Weak correlation

• Strong correlation

Which function is used to find the correlation values before plotting a heat map?

• corr()

• sum()

• mean()

• count()

What does the 'isna' method return when applied to a dataset?

• The mean of each column

• The number of unique values

• A boolean indicating null status

• The sum of all values