People Analytics Case I

Effective Onboarding Process & Early Attrition Analysis

Open in Google Colab

Business Context

Early attrition during the probation period generates significant cost and signals onboarding inefficiencies.

This project evaluates onboarding effectiveness and its impact on employee exit during the first months.

Objective

Analyze whether onboarding quality indicators are associated with early attrition risk.

Methodology

• Exploratory Data Analysis (EDA)
• Distribution and segmentation analysis
• Relationship assessment between onboarding variables and exit
• Predictive modeling (if applicable)

Key Business Questions

• Does onboarding quality influence probation turnover?
• Are certain segments more vulnerable?
• Can early risk signals be identified?

Business Impact

• Improve onboarding programs
• Reduce early turnover
• Enable proactive HR interventions

Tech Stack

Python, Pandas, Scikit-learn, Matplotlib

Results

• Identification of key onboarding indicators linked to early attrition.
• Segmentation of higher-risk employee groups during probation.
• Clear analytical framework to support onboarding optimization decisions.

Ethical Considerations

When applying People Analytics in HR contexts:

• Avoid discriminatory bias in predictive modeling.
• Ensure transparency in how insights are used.
• Protect employee privacy and sensitive data.
• Use analytics as decision-support, not decision-automation.

Responsible AI in HR is critical for sustainable workforce management.