Title: "Predicting Employee Attrition with Logistic Regression: A Data-Driven HR Analytics Approach"
Description: This project employs advanced data analytics techniques to predict employee attrition within organizations using logistic regression. Employee turnover poses a significant challenge for businesses, impacting productivity and organizational performance. By harnessing the power of machine learning, this project aims to develop a robust attrition prediction model that leverages features such as age, job satisfaction, work environment, and more. Through exploratory data analysis, feature engineering, and model tuning, we seek to uncover insights into the underlying drivers of attrition and enhance predictive accuracy. Model tuning, including hyperparameter optimization via grid search, will be employed to fine-tune the logistic regression model and optimize performance metrics such as F1-score, recall, and precision. Furthermore, the project will utilize both Seaborn and Plotly libraries to visualize relationships between different features and attrition, providing actionable insights for HR professionals.
Objective:
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Develop a logistic regression model capable of accurately predicting employee attrition based on a diverse range of HR-related features.
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Employ model tuning techniques, including grid search, to optimize hyperparameters and maximize performance metrics such as F1-score, recall, and precision.
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Utilize exploratory data analysis to uncover insights into the underlying drivers of attrition and identify key factors influencing employee turnover.
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Leverage Seaborn and Plotly libraries to visualize relationships between different features and attrition, facilitating a deeper understanding of employee behavior and engagement.
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Provide actionable insights and recommendations to HR professionals, empowering them to implement proactive retention strategies and foster a positive work environment conducive to employee satisfaction and organizational success.