This repository includes six real-world case studies in data science applied to business scenarios.
Employee attrition is a significant challenge for organizations, leading to high costs and reduced productivity. This case study focuses on using machine learning to predict employee attrition and empower HR teams to make proactive, data-driven decisions.
- High Costs of Hiring: Recruiting a new employee costs 15%-20% of their salary, with an average of $7,645 for small companies.
- Retention Challenges: Understanding factors like job satisfaction and work-life balance is critical.
- Need for Prediction: An ML-based model is required to identify employees likely to quit.
- Cost Savings: Minimize hiring costs and revenue losses.
- Increased Productivity: Retain top talent and reduce turnover.
- Strategic Planning: Optimize workforce management using model insights.
- Includes variables such as job involvement, education, job satisfaction, performance ratings, and work-life balance.
- Models like logistic regression, random forests, and neural networks will be used to predict attrition and support HR strategies.
Explore this case study to see how data science can transform HR challenges into actionable solutions!