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 applied to identify null values in a data frame?

How do you calculate the percentage of null values in each field?

What threshold is used to filter fields with a high percentage of null values?

What actions can be taken when dealing with missing values in predictive modeling?

What is the significance of categorical variables in the data set?

How are unique values and their frequencies determined for categorical features?

What challenges are associated with the interest rate being treated as a categorical feature?

What is the purpose of creating bins for annual income?

How is the correlation between annual income and default ratio analyzed?

What insights can be derived from the default ratio across different income brackets?