Practical Data Science using Python - EDA Project - 5

The video tutorial explains how to create categorical features from numeric data, focusing on funded amounts and annual income. It analyzes default ratios across different funded amount ranges and visualizes the relationship between funded amounts and income brackets. The tutorial also examines mean values of numeric features for defaulted and non-defaulted loans, and compares requested loan amounts with disbursed amounts to identify discrepancies. Insights are provided for potential improvements in loan default rates.

What is the purpose of creating the 'if amount' feature from the funded amount?

• To remove outliers from the data

• To decrease the funded amount

• To increase the funded amount

• To convert numeric data into categorical data

How is the 'if amount' feature populated?

• By applying a user-defined function to the funded amount

• By manually entering values

• By using a random number generator

• By using a machine learning model

What observation is made about loans with funded amounts greater than 20K?

• They are never approved

• They have the lowest default ratio

• They have the highest default ratio

• They are always approved

In the analysis of funded amount and annual income, what was the default ratio for the income bracket 0 to 30K and funded amount 20K+?

• 75%

• 50%

• 25%

• 10%

What is the significance of the 50% default ratio observed in the analysis?

• It represents a large sample size

• It is due to a small sample size

• It indicates a data error

• It shows a trend across all data

What is the purpose of using visualizations in the analysis of funded amount and annual income?

• To increase data complexity

• To visually identify relationships and default ratios

• To hide data discrepancies

• To reduce data size

What conclusion is drawn from the mean value analysis of defaulted and non-defaulted groups?

• There are no significant differences in mean values

• Defaulted groups have higher mean values

• Non-defaulted groups have higher mean values

• There are significant differences in mean values

What is the main focus of the analysis in the section on mean values?

• To identify the most common loan types

• To compare mean values of numeric features between defaulted and non-defaulted groups

• To calculate the total number of loans

• To find the highest loan amounts

What does a 0% difference between loan amount and funded amount indicate?

• The loan was not approved

• The loan was rejected

• The entire requested amount was disbursed

• The loan was partially approved

What might a significant difference between requested and disbursed amounts suggest?

• A problem with the loan application process

• A successful loan application

• A high credit score

• A low interest rate