Exploratory Data Analysis is a data analytics process to understand the data in depth and learn the different data characteristics, often with visual means.

It is used to discover trends, and patterns, or check assumptions, gather insights and make better sense of the data, and remove irregularities and unnecessary values from data.

Objective of EDA

• Identifying and removing data outliers

• Identifying trends in time and space

• Uncover patterns related to the target

• Creating hypotheses and testing them through experiments.

• Identifying new sources of data.

Steps involved in EDA

Types of EDA

Univariate Analysis

In Univariate analysis, the output is a single variable and all data collected is for it. There is no cause-and-effect relationship at all.

Bivariate Analysis

In Bivariate analysis, the outcome is dependent on two variables, while the relation with it is comparted with two variables.

Multivariate Analysis

In multivariate analysis, the outcome is more than two. The analysis of data is done on variables that can be numerical or categorical. The result of the analysis can be represented in numerical values, visualization, or graphical form.

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That’s it… Hope you liked this article. Happy Learning:).

Feel free to ask your doubts in the comments.