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HR Attrition Analytics — Complete Project Guide

SECTION 01Project overview

Here's what you'll build in this project:

• Business problem: A company is experiencing high employee turnover and wants to understand why employees are leaving and how to improve retention.
• Your solution: An attrition analytics dashboard that tracks key metrics — attrition rate, tenure, satisfaction, salary — and identifies key drivers.
• Tools: Python (pandas, matplotlib, seaborn), Tableau/Power BI, or R.
• Outcome: A portfolio-ready project that demonstrates HR analytics and business strategy skills.

SECTION 02Data source

Here are the best data sources for this project:

SECTION 03Key metrics and KPIs

Here are the key metrics your HR attrition dashboard should track:

SECTION 04Attrition drivers

Here are the key drivers of attrition you should analyze:

• Job satisfaction: Employees with low satisfaction are more likely to leave.
• Tenure: The first 1-2 years have the highest attrition risk.
• Salary: Underpaid employees are more likely to leave.
• Work-life balance: Poor work-life balance drives turnover.
• Career development: Lack of growth opportunities leads to attrition.
• Department: Some departments have higher turnover.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv('ibm_hr_attrition.csv')

# Attrition rate
attrition_rate = df['Attrition'].value_counts(normalize=True)['Yes']
print(f"Attrition Rate: {attrition_rate:.2%}")

# Attrition by department
dept_attrition = df.groupby('Department')['Attrition'].apply(
    lambda x: (x == 'Yes').mean()
).sort_values(ascending=False)
print("\nAttrition by Department:")
print(dept_attrition)

# Attrition by tenure
tenure_attrition = df.groupby('YearsAtCompany')['Attrition'].apply(
    lambda x: (x == 'Yes').mean()
)
plt.figure(figsize=(10,6))
sns.barplot(x=tenure_attrition.index, y=tenure_attrition.values)
plt.title('Attrition Rate by Tenure')
plt.xlabel('Years at Company')
plt.ylabel('Attrition Rate')
plt.show()

# Correlation analysis for attrition drivers
# Convert attrition to numeric
df['attrition_num'] = (df['Attrition'] == 'Yes').astype(int)

# Select numeric features
numeric_cols = ['Age', 'DistanceFromHome', 'Education', 'JobSatisfaction',
                'MonthlyIncome', 'NumCompaniesWorked', 'PercentSalaryHike',
                'TotalWorkingYears', 'YearsAtCompany', 'YearsWithCurrManager']

correlations = df[numeric_cols + ['attrition_num']].corr()['attrition_num'].sort_values()

print("Top drivers of attrition:")
print(correlations.tail(10))

SECTION 05Dashboard design

Here's how to design your HR attrition dashboard:

• Top section: KPI cards — Attrition Rate, Avg Tenure, Employee Count, Satisfaction Score.
• Middle section: Attrition by department, attrition by tenure, satisfaction distribution.
• Bottom section: Correlation heatmap, salary distribution by attrition status.
• Filters: Department, tenure range, job role.

SECTION 06Portfolio presentation

Here's how to present this project to employers:

• GitHub: Upload your code, data preparation scripts, and dashboard file.
• README: Write a clear README with project overview, key drivers, and recommendations.
• Executive summary: Include a 1-page summary for business stakeholders.
• Screenshots: Add screenshots of your dashboard and key insights.
• LinkedIn post: Share your project with a brief explanation of the business problem you solved.

SECTION 07Interview Q&A — HR attrition analytics

SECTION 08Test yourself — HR attrition quiz

SECTION 09Frequently asked questions

SECTION 10Related reads