HR Analytics Project (Employee Attrition & Workforce Insights)

Project Roadmap

This roadmap summarizes how the HR Analytics project was carried out:

This project applies HR Analytics to study workforce trends, employee engagement, and attrition risk.
It uses a real HR dataset (1,470 employees, 39 features) and combines:

• Python Analytics (KPIs, predictive modeling)
• Excel Dashboard (interactive HR insights)
• Enterprise HRIS context (SAP, Workday, SuccessFactors)

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Business Understanding

Core HR Questions addressed in this project:

1. What is the overall attrition rate and how does it vary by department, gender, and age group?
2. Which job roles and education fields experience the highest attrition?
3. How do factors like salary, overtime, and job satisfaction influence attrition?
4. What are the key predictors of attrition that HR managers should monitor?
5. How can HR leaders use these insights for employee retention, workforce planning, and engagement?

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Dataset

The dataset used for this project is available here:
➡️ Download HR Dataset (Excel)

• Employees: 1,470
• Features: 39
• Examples: Age, Gender, Department, Job Role, Monthly Income, Marital Status, Overtime, Performance Rating, Years at Company, Work-Life Balance, etc.
• Target variable: Attrition (Yes/No)

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Key KPIs

From the dataset and dashboard:

• Employee Count: 1,470
• Attrition Count: 237
• Attrition Rate: 16.12%
• Active Employees: 1,233
• Average Age: 37 years
• Attrition by Gender: Male – 150 | Female – 87
• Attrition by Department:
  • R&D: 133 employees (56%)
  • Sales: 92 employees (39%)
  • HR: 12 employees (5%)

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Dashboard Preview

Excel Dashboard

A clean, interactive HR Dashboard built in Excel to visualize key workforce KPIs, attrition patterns, and demographics.

How the Excel Dashboard Was Built

The HR Analytics Excel Dashboard was developed using raw data from data/HR Analytics.xlsx.

Steps followed:

1. Cleaned and structured employee data using Excel formulas and filters.
2. Created Pivot Tables for key KPIs — attrition, department, gender, and job role.
3. Linked pivot tables to dynamic charts (bar, pie, and donut).
4. Used slicers to make the dashboard interactive (by department, education, and gender).
5. Applied custom formatting and icons for professional visual presentation.

This dashboard provides clear visibility into employee attrition trends, department performance, and demographic patterns.

Here is the HR Analytics Dashboard created in Tableau:

Features:

• Department-wise attrition
• Education field-wise attrition
• Attrition by gender & age group
• Job satisfaction rating by role
• Employee count distribution by age

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Insights

From the analysis of 1,470 employees:

• Attrition Rate: ~16% (237 employees left)
• Overtime: Strongly correlated with attrition — employees working overtime are more likely to leave.
• Salary: Lower income employees are at higher risk of attrition.
• Job Satisfaction: Employees with low satisfaction are much more likely to resign.
• Tenure: Short-tenure employees are more prone to leaving.
• Business Travel: Frequent travel increases attrition risk.

Recommendations

1. Overtime Policy: Reduce excessive overtime and promote work-life balance.
2. Compensation: Review pay structure for lower-income roles to improve retention.
3. Engagement Programs: Target employees with low job satisfaction through surveys, mentoring, and recognition.
4. Onboarding & Early Career Support: Provide additional support to new hires to reduce early attrition.
5. Travel Flexibility: Offer hybrid/remote options or travel support for employees who travel frequently.
6. HRIS/SAP Integration: Feed attrition risk scores into HR dashboards (e.g., SAP SuccessFactors, Workday) for proactive interventions.

Predictive Modeling (Python)

A logistic regression model was trained to predict attrition:

• Accuracy: ~78% (varies by split)
• Key Predictors of Attrition:
  • Overtime (employees with overtime more likely to leave)
  • Job Satisfaction (low = higher risk)
  • Monthly Income (lower salaries = higher risk)
  • Business Travel (frequent = higher risk)
  • Years at Company (shorter tenure = higher risk)

Outputs:

• outputs/kpi.json – KPI summary
• outputs/attrition_by_department.png – attrition by department
• outputs/income_by_attrition.png – salary impact
• outputs/years_vs_income.png – tenure vs income
• outputs/model_report.txt – classification report & confusion matrix
• outputs/top_feature_coefficients.csv – top predictors

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Tech & Tools

• Excel → Interactive HR dashboard
• Python (pandas, matplotlib, scikit-learn) → data wrangling, visualization, predictive model
• HRIS Integration (conceptual) → results could feed into SAP SuccessFactors, Workday, Oracle HCM
• Future BI → Power BI or Tableau for executives

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How to Run the Python Analysis

# 1) Clone this repository:
   ```bash
   git clone https://github.com/Egbe34/HR-analytics.git
   cd HR-analytics