This repository showcases my completed project on Employee Attrition Analysis, developed as part of the Google Advanced Data Analytics Professional Certificate. The project employs a structured, data-driven approach to identifying key drivers of employee turnover and providing actionable HR strategies to improve retention.
The project spans the entire analytics lifecycle, including:
- Defining Objectives: Identifying business challenges, aligning stakeholder needs, and setting clear goals.
- Planning: Structuring workflows, selecting methodologies, and defining key performance indicators.
- Development: Building Jupyter Notebooks to clean data, engineer features, and develop predictive models.
- Visualization: Full Exploratory Data Analysis visualizing insights and trends.
- Delivery: Crafting an executive report summarizing key findings, model performance, and actionable HR recommendations.
The goal of this project was to develop a predictive model for employee attrition, helping Salifort Motors understand and mitigate workforce turnover. By leveraging machine learning, the analysis identifies tenure-based attrition risks, workload burnout factors, and career stagnation triggers.
This repository highlights real-world HR applications of data analytics, demonstrating expertise in feature engineering, model evaluation, and strategic decision-making. The insights gained empower HR teams with data-driven retention strategies, reducing hiring costs and improving workforce stability.
- Project Workflow
- Outlines the structured approach taken in this project, detailing key phases from project proposal to model deployment.
- PACE Strategy Document
- A high-level strategic plan defining the problem, objectives, methodology, and expected business impact of the analysis.
- Project Proposal
- Formal proposal document outlining the project’s scope, key deliverables, stakeholder considerations, and timeline.
- Data Cleaning - Jupyter Notebook
- Documents the preprocessing steps applied to ensure data accuracy, handling missing values, feature engineering, and dataset transformations.
- EDA & Visualization - Jupyter Notebook
- Explores key trends and patterns in employee attrition, using visualizations and statistical insights to inform model development.
- Model Development - Jupyter Notebook
- Details the predictive modeling process, including feature selection, model training (logistic regression, decision trees, random forest, and XGBoost), hyperparameter tuning, and evaluation.
- Final Executive Report
- A concise business report summarizing findings, model insights, and actionable HR recommendations for mitigating attrition risks.
- Addtional Slide Presentation
- A LinkedIn-ready carousel slide deck summarizing key project insights in an engaging, visual format.
License: All rights reserved. No part of this repository may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the owner.