Practical Data Science using Python - EDA Project - 4

The video tutorial covers handling null values in data frames, analyzing categorical variables, and performing feature engineering through binning. It explains how to identify fields with high null percentages, analyze unique values in categorical data, and create bins for numeric features like annual income to study their correlation with default ratios. The tutorial also discusses the implications of these analyses for predictive modeling and decision-making.

What function is used to identify null values in a dataset?

• notnull

• dropna

• isnull

• fillna

What is the purpose of dividing the sum of null values by the shape of the dataset?

• To remove null values from the dataset

• To identify the columns with null values

• To find the total number of null values

• To calculate the percentage of null values

Which data type is used to identify categorical variables in a dataset?

• bool

• object

• float64

• int64

Why is the interest rate considered an object data type?

• It contains decimal values

• It includes a percentage sign

• It is a categorical variable

• It is a constant value

What is the purpose of creating bins for numeric features?

• To convert numeric data into strings

• To simplify data visualization

• To categorize data into ranges

• To remove outliers from data

What is the main goal of analyzing binned data?

• To identify missing values

• To find correlations with default ratios

• To calculate average income

• To determine data types

Which function is used to create a matrix of counts for binned data?

• pivot_table

• merge

• groupby

• crosstab

What does a higher default ratio in a specific income bin indicate?

• Lower interest rates

• Higher risk of default

• More loans are approved

• Fewer loans are defaulted

How is the default ratio calculated for each income bin?

• By adding defaulted and non-defaulted loans

• By subtracting non-defaulted loans from total loans

• By dividing defaulted loans by total loans

• By dividing total loans by defaulted loans

What insight can be drawn from the default ratio analysis?

• Default ratios are irrelevant to income

• All income brackets have the same default ratio

• Lower income brackets have higher default ratios

• Higher income leads to more defaults