What are the different techniques used in EDA?

Data Science, AI and ML
#1

EDA focuses more narrowly on checking assumptions required for model fitting and hypothesis testing. It also checks while handling missing values and making transformations of variables as needed, filling the counts. EDA builds a robust understanding of the data, and issues associated with either the info or process. it’s a scientific approach to getting the story of the data.

TYPES OF EXPLORATORY DATA ANALYSIS:

  1. Univariate Analysis: Uni means one and variate means variable, so in univariate analysis, there is only one dependable variable. The objective of the univariate analysis is to derive the data, define and summarize it, and analyze the pattern present in it. In a dataset, it explores each variable separately.
  2. Bivariate Analysis: It is performed to find the relationship between each variable in the dataset and the target variable of interest (or) using 2 variables and finding the relationship between them.
  3. Multivariate Analysis: Graphical methods summarize the data in a diagrammatic or visual way. Univariate methods look at one variable (data column) at a time, while multivariate methods look at two or more variables at a time to explore relationships. Usually, multivariate EDA will be bivariate (looking at exactly two variables).