Attrition Unveiled: Analyzing Trends and Strategies in Employee Turnover

Aman Shrivastava; Dr Pooja Khanna

doi:10.54060/a2zjournals.jmss.72

Authors

Aman Shrivastava Department of Computer Science & Engineering, Amity School of Engineering & Technology Lucknow, Amity University Uttar Pradesh, India
Dr Pooja Khanna Department of Computer Science & Engineering, Amity School of Engineering & Technology Lucknow, Amity University Uttar Pradesh, India

DOI:

https://doi.org/10.54060/a2zjournals.jmss.72

Keywords:

Employee attrition, EDA, Retention strategies, data visualisation, Python, Turnover

Abstract

This study performs an Exploratory Data Analysis (EDA) on employee attrition data using Python to identify key factors influencing turnover. Employee attrition affects organizational performance, making it crucial to understand its root causes for effective retention strategies. The analysis considers various employee attributes, including demographics (age, gender) and job-related factors (salary, satisfaction, tenure). The process involves data cleaning, followed by univariate, bivariate, and multivariate analyses to explore variable relationships. Visualizations such as bar charts, scatter plots, and heatmaps aid in identifying patterns and high-risk employees. The findings offer actionable recommendations to enhance retention and serve as a foundation for an organization to make data-driven decisions.

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