📌 HR Analytics Dashboard

This project analyzes employee attrition using a comprehensive HR dataset to identify the key factors and trends driving employees to leave the organization. The goal is to provide actionable insights for developing targeted retention strategies.

📂 Datasets Information

Key Columns:
a. EmpID → Employee ID (Unique Identifier)
b. Age → Age of the employees
c. Gender → Male / Female
d. Attrition → Employee Attrition Status (Yes/No)
e. MonthlyIncome → Monthly salary of the employee
f. JobRole → Role held by the employee in the company
g. YearsAtCompany → Total years worked at the current company
h. EducationField → Field of education (e.g., Life Sciences, Medical)

Other Columns: DailyRate, Department, BusinessTravel, DistanceFromHome, JobLevel, WorkLifeBalance, TotalWorkingYears, OverTime, MaritalStatus, PercentSalaryHike

🎯Problem Statement

The organization is experiencing a significant employee Attrition Rate of 16.1%. The core problem is to identify the underlying demographic, job-related, and environmental factors that have the highest correlation with attrition. The project must pinpoint who is leaving, why, and when (in their tenure) to recommend data-driven intervention plans.

📊Objectives & Analysis

Objective: To build an interactive HR Analytics Dashboard that provides a clear, quantitative view of employee attrition patterns across salary, job role, tenure, and other critical dimensions.

Analysis Approach:

1. Data Cleaning & Preprocessing: Ensure data quality by handling duplicates, standardizing inconsistent values, and validating data types.
2. Measure & KPI Calculation: Create essential metrics for reporting, such as the overall Attrition Rate and specific Attrition Counts.
3. Visual Exploration & Analysis: Visualize attrition trends across key categorical and numerical variables to extract actionable insights.

🔍Key Insights

1. Overall Attrition: The company's total employee base is 1470, with 237 departures, resulting in an Attrition Rate of 16.1%.
2. Salary is a Key Driver: The highest volume of attrition (163 employees) occurred in the 'Up to 5K' salary slab, indicating that low compensation is the primary reason for leaving, especially among those paid below the average salary of 6.50K.
3. Most Affected Age Group: The 26-35 age group is responsible for the highest number of departures with 116 attritions, signaling a high loss of critical mid-career talent.
4. Top Attrition Job Roles: The top 4 job roles with the highest turnover are Laboratory Technician (62), Sales Executive (57), Research Scientist (47), and Sales Representative (33).
5. Attrition by Education Field: Life Sciences majors account for the largest proportion of total attrition at 37.55%.
6. Early Tenure Peak: Attrition peaks significantly in the first year (Year 0) with 18 departures, suggesting issues with onboarding or role fit.
7. Mid-Career Stagnation: A second major attrition peak occurs around 5 years of service, suggesting a potential lack of promotional opportunities or career growth.
8. Long-Term Loyalty Issues: A notable peak in attrition is also observed at 10 years of service, potentially indicating burnout or seeking new challenges after a long tenure.

🧠Skills Gained

Through this case study, I gained hands-on experience in:

1.Data Cleaning – removing duplicates (EmpID), replacing values (BusinessTravel), and checking Data Type.
2.Measure Creation – Calculating critical business metrics like Attrition Rate (Total Attrition/Total Employees).
3.Calculated Column Creation – Implementing AttritionCount for card visuals, requiring data type conversion from Text to Whole Number.
4.Data Transformation – Standardizing categorical data values, specifically replacing TravelRarely with Travel_Rarely.
5.Key Performance Indicator (KPI) – Calculating and displaying summary statistics like Total Employees (1470) and Total Attrition (237).
6.Advanced Data Filtering – Applying specific context filters for visualization (e.g., creating a visual for the TOP 4 job roles in attrition).
7.Visual Design & Charting – Creating effective visualizations like Area Charts (Attrition by Years at Company) and Bar Charts.
8.Business Insight Interpretation – Translating dashboard metrics into actionable business recommendations (e.g., addressing low salary and tenure-specific retention issues).