Exploratory Data Analysis (EDA) is a method used in machine learning to understand and analyze data sets in detail. EDA is typically performed as a preliminary step before undertaking more formal statistical analyses or modeling. The main goals of EDA include understanding the predominant traits of the data, discovering patterns, locating outliers, and identifying relationships between variables. EDA can also help with feature engineering, data segmentation, and hypothesis generation.
The main purpose of EDA is to help look at data before making any assumptions. It can help identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, and find interesting relations among the variables. EDA can also help determine if the statistical techniques you are considering for data analysis are appropriate.
Some specific statistical functions and techniques that can be performed with EDA include calculating frequency counts, visualizing distributions, creating scatterplots, and calculating correlations. EDA is typically part of every machine learning or predictive modeling project, especially with tabular datasets.
In summary, EDA is a crucial step in machine learning that helps data scientists understand and analyze data sets in detail before undertaking more formal statistical analyses or modeling.
