Employee-Attrition-Prediction Data Analysis

In this project, I performed an in-depth analysis of employee attrition data using SQL, focusing on understanding the demographics of the workforce and their impact on retention rates. The goal was to identify key factors contributing to employee turnover and provide actionable insights to help reduce attrition rates. By combining demographic analysis with retention rate calculations, I was able to provide a comprehensive view of the factors influencing employee attrition, leading to targeted recommendations for improving employee retention.

Key Objectives

• Data Exploration:

Analyze employee demographics, job roles, and organizational factors to gain a comprehensive understanding of the workforce. Assess attributes such as age, gender, marital status, education level, and experience to identify patterns and trends in employee composition.

• Trend Identification:

Identify relationships between factors such as age, job satisfaction, salary, and department to uncover potential predictors of employee attrition. Focus on understanding how specific demographic groups or job roles are correlated with higher turnover rates.

• Data Manipulation:

Utilize advanced SQL skills to retrieve, filter, aggregate, and manipulate employee data, enabling efficient and accurate analysis. Perform tasks such as calculating retention rates, segmenting the workforce by various attributes, and generating summary statistics.

• Schema Optimization:

Evaluate the database schema for efficiency, redundancy, and data integrity. Propose improvements to enhance data quality, streamline data retrieval processes, and ensure the robustness of the analysis.

Technical Stack

• Database Management System (DBMS): PostgreSQL
• SQL dialect: Standard SQL

Methodology

• Data Acquisition:

  Load the provided Employee attrition dataset into PostgreSQL which includes demographics, job roles, performance metrics, and attrition status.

• Data Exploration:

Conducted exploratory data analysis (EDA) to understand the distribution of key variables such as age, department, job satisfaction, and tenure. Visualized data using SQL queries to identify initial patterns and correlations.

• Trend Analysis:

Analyzed relationships between demographic factors, job roles, and attrition rates to uncover potential predictors of turnover. Segmented the data to examine retention rates across different departments, job levels, and employee demographics.

• SQL Data Manipulation:

Applied SQL techniques for data retrieval, filtering, and aggregation to generate insights. Calculated metrics such as retention rates, average tenure, and attrition trends by various employee segments.

• Schema Evaluation:

Reviewed the database schema for redundancy and integrity. Suggested optimizations to improve data quality, reduce redundancy, and enhance performance.

Project Deliverables:

• Dataset folder that contains Employee attrition dataset in CSV format
• SQL script file containing all queries used for data analysis, manipulation, and optimization.
• Project PDF document showcasing the executed queries and their corresponding outputs, along with interpretations and insights gained from the analysis.

Target Audience:

• HR professionals responsible for employee engagement, retention strategies, and workforce planning. The insights from this analysis can help them develop targeted interventions to reduce attrition and improve employee satisfaction.

• Executives and managers who need to understand the underlying factors driving employee turnover. The analysis provides them with data-driven insights to inform strategic decisions and optimize workforce management.

• Analysts interested in employee data and workforce analytics. This project showcases advanced SQL techniques and analysis methodologies that can be applied to similar datasets to derive actionable insights.

Future Work:

• Implement machine learning models to forecast employee attrition and identify at-risk employees.
• Create dashboards to visualize key attrition metrics and track trends for real-time insights.
• Integrate additional data sources and continuously monitor attrition metrics to refine analysis and improve retention strategies

In summary, this project has demonstrated the power of SQL in analyzing employee attrition data, uncovering key factors influencing turnover, and providing actionable insights. By leveraging predictive modeling and interactive dashboards, we can effectively identify at-risk employees and monitor retention trends. The project sets a strong foundation for further exploration and refinement of strategies aimed at enhancing employee retention and fostering a more stable workforce.