Exploratory Data Analysis (EDA) is an approach for data analysis that employs a variety of techniques (mostly graphical) to
- maximize insight into a data set;
- uncover underlying structure;
- extract important variables;
- detect outliers and anomalies;
- test underlying assumptions;
- develop parsimonious models; and
- determine optimal factor settings.
As a Machine Learning engineer, one of the first steps implemented as part of a machine learning project is Exploratory Data Analysis.
Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns, spot anomalies, test hypotheses, and check assumptions with the help of summary statistics and graphical representations.
Types of exploratory data analysis
There are four primary types of EDA:
- Univariate non-graphical: This is the simplest form of data analysis, where the data being analyzed consists of just one variable. Since it’s a single variable, it doesn’t deal with causes or relationships. The main purpose of the univariate analysis is to describe the data and find patterns that exist within…
