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 significance of establishing whether features are normally distributed in EDA?

Describe the role of heat maps in exploratory data analysis.

What are some common visualization techniques used in EDA?

In the context of Lending Club, what features might lead to a higher rate of loan defaults?