GitHub / Cyberoctane29 / Salifort-Motors-Predicting-Employee-Turnover-and-Improving-Retention-Analysis-and-Modeling
In this project, I work as a data analytics professional at Salifort Motors, a fictional leader in alternative energy vehicles. I analyze employee survey data to identify turnover drivers and build predictive models, including multiple logistic regression, decision trees, and random forests, to forecast attrition and support retention strategies.
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PURL: pkg:github/Cyberoctane29/Salifort-Motors-Predicting-Employee-Turnover-and-Improving-Retention-Analysis-and-Modeling
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Forks: 0
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License: None
Language: Jupyter Notebook
Size: 28.7 MB
Dependencies parsed at: Pending
Created at: 4 months ago
Updated at: 3 months ago
Pushed at: 3 months ago
Last synced at: 24 days ago
Topics: data-analytics, data-visualization, decision-trees, eda, employee-attrition, ethical-artificial-intelligence, feature-engineering, logistic-regression, machine-learning, random-forest, regression-analysis, statistical-analysis, supervised-learning, turnover-analysis