This project offers a comprehensive analysis and predictive modeling approach to support the Human Resources (HR) department of a large consulting firm. By examining employee data, the project aims to identify factors influencing employee turnover and develop models that predict whether an employee is likely to leave the company. Such insights empower HR professionals to make informed decisions to improve retention and better allocate resources.

The project is organized into two main components:

1. One-Page Summary: A concise, one-page overview of the project, designed for presentation to external stakeholders. This summary highlights the key findings, methodology, and the potential impact of the analysis on HR practices, prepared specifically for Salifort Motors.

2. Complete Code Notebook: A detailed, fully-documented Jupyter Notebook containing the data preprocessing, exploratory analysis, model building, and evaluation stages. This notebook includes both regression and machine learning models to predict employee turnover, with thorough explanations of each step.

• Predict Employee Turnover: Build and evaluate models that can accurately predict if an employee is likely to leave, based on historical data.
• Identify Key Factors: Pinpoint important features contributing to turnover, helping HR understand what drives employees to stay or leave.
• Provide Actionable Insights: Offer insights that can help HR develop targeted retention strategies.

The final deliverables include:

• Model Evaluation: Comprehensive assessment of model performance using metrics like accuracy, precision, recall, and F1-score.
• Data Visualizations: Clear and insightful visualizations directly addressing the questions posed, helping to interpret the results and communicate findings to stakeholders.
• Ethical Considerations: A discussion on ethical implications, including privacy concerns, data handling, and fairness in predictive modeling.
• Troubleshooting Resources: A list of resources consulted to address challenges encountered during the project, ensuring transparency in the solution-finding process.

• Data Analysis and Visualization: Pandas, NumPy, Matplotlib, Seaborn
• Machine Learning: Scikit-learn (for regression and classification models)
• Notebook Environment: Jupyter Notebook

This project equips HR professionals with predictive insights into employee turnover, offering a powerful tool for retention strategy development. By combining rigorous data analysis with ethical considerations, the project provides a responsible and impactful approach to understanding employee behavior.

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