Practical Data Science using Python - EDA Project - 2

The video tutorial covers the use of box plots and quantile functions to analyze loan and income data. It explains how to interpret box plots, handle outliers, and perform exploratory data analysis (EDA). The tutorial also discusses univariate analysis of loan amounts and interest rates, highlighting the importance of understanding data distribution and patterns.

What does a box plot visually represent in data analysis?

• Only the maximum and minimum values

• The correlation between two variables

• The mean and median of the data

• The distribution of data including quartiles and outliers

What is the significance of the 25th quartile in a box plot?

• It represents the median of the data

• It shows the maximum value

• It indicates the lower 25% of the data

• It highlights the outliers

How can the quantile function be used in data analysis?

• To visualize data distributions

• To calculate the mean of a dataset

• To find specific percentile values in a dataset

• To determine the mode of a dataset

What is the primary goal of exploratory data analysis?

• To confirm hypotheses

• To explore and identify useful patterns in data

• To create predictive models

• To clean and preprocess data

Why might removing outliers be beneficial when analyzing data distributions?

• It increases the sample size

• It always leads to more accurate results

• It helps in visualizing the true distribution of data

• It reduces the complexity of calculations

What was observed about the correlation between annual income and default ratio?

• The correlation varies significantly

• There is a strong negative correlation

• There is a strong positive correlation

• There is no apparent correlation

How can outliers affect the visualization of data distributions?

• They always improve the accuracy of the data

• They have no effect on visualization

• They can skew the scale, making it hard to interpret

• They make the data appear more uniform

What is the purpose of analyzing the distribution of loan amounts and interest rates?

• To identify fraudulent loans

• To predict future interest rates

• To understand the patterns and concentrations in the data

• To determine the average loan amount

What does a spike in the interest rate distribution indicate?

• A sudden increase in loan defaults

• A common interest rate at which many loans were disbursed

• A decrease in loan applications

• An error in data collection

What does the occurrence density in a distribution plot represent?

• The ratio of occurrences of different values

• The average value of the dataset

• The total number of data points

• The frequency of a specific value