Practical Data Science using Python - EDA Tools and Processes

The video tutorial provides an in-depth exploration of Exploratory Data Analysis (EDA), focusing on its dual role in analytics and machine learning. It explains how EDA helps in identifying data patterns, trends, and insights aligned with business goals. The tutorial distinguishes between univariate and multivariate EDA, highlighting various visualization techniques like scatter plots and heat maps. It also outlines the EDA process, including feature analysis and outlier detection. A practical case study on Lending Club demonstrates EDA's application in identifying factors leading to loan defaults.

What is the primary goal of EDA when used for analytics?

• To find patterns and insights for business goals

• To prepare data for machine learning

• To clean and transform data

• To create predictive models

How does EDA differ when its goal is machine learning modeling?

• It ignores missing values

• It uses only graphical methods

• It emphasizes data preparation and cleaning

• It focuses on generating reports

What is univariate analysis?

• Analysis of time series data

• Analysis of categorical data

• Analysis of multiple features

• Analysis of a single feature

Which method is used to visualize the distribution of a single feature?

• Scatter plot

• Bubble chart

• Boxplot

• Heat map

What is the purpose of a heat map in EDA?

• To show the distribution of a single feature

• To capture pairwise relationships between features

• To display time series data

• To visualize categorical data

What is the first step in the high-level EDA process?

• Feature engineering

• Data cleaning

• Outlier detection

• Correlation analysis

Which of the following is a goal of predictive modeling in EDA?

• To find missing values

• To summarize data

• To create visualizations

• To solve complex problems using machine learning

What problem is Lending Club facing in the case study?

• Low loan approval rates

• High default rates

• Inaccurate credit scores

• Poor customer service

What is the main objective of the Lending Club case study?

• To improve customer satisfaction

• To reduce interest rates

• To increase loan amounts

• To identify features leading to high default rates

Which analysis technique is used to find relationships between multiple features?

• Univariate analysis

• Bivariate analysis

• Time series analysis

• Multivariate analysis