Graphical Exploratory Data Analysis (GEDA): A Case Study on Employee Attrition
DOI:
https://doi.org/10.46243/jst.2022.v7.i09.pp01-11Keywords:
seaborn, matplotlib, Data Visualization, Python, Graphical Exploratory Data AnalysisAbstract
Exploratory Data Analysis (EDA) popularly performs some preliminary investigations on the dataset to understand its content and structure. EDA is a mandatory step in the complete process of data analysis, since its mandatory to analyze the data in order to produce good results and in turn help in decision making. There are several Graphical EDA techniques which not only analyze the data but also present the results in graphical form. This paper uses the Python programming language for both data analysis and visualization of results. The rich set of python libraries including pandas, numpy, matplotlib, seaborn etc greatly supports the process of GEDA. This paper works on the “Employee Performance and Attrition” dataset to analyze and extract potential information and present results in graphical form.
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FIRST EDITION 2021.BPB Publications
