##Employee Attrition Analysis Using Databricks - SQL & Data Visualization I used Databricks to perform exploratory analysis and data visualization to explore patterns and causes of attrition within a company, leveraging descriptive statistics and SQL queries. This approach helped to gain insights into the key factors influencing employee attrition, such as age, department, education, satisfaction, and business travel.
Databricks SQL: For running SQL queries to aggregate and analyze employee attrition data. Data Visualization: To visualize the findings, I used various charts like Bar Charts, Pie Charts, and Stacked Bar Charts in Databricks to better understand the data patterns.
Age Group: Attrition rates were higher among younger employees (aged 20-32 years). Department: Departments such as Sales and HR showed significantly higher attrition rates compared to others. Education Level: Employees with lower educational attainment (below a college degree) had higher attrition rates. Environment Satisfaction: Employees with lower environment satisfaction were more likely to leave the company. Business Travel: Business travelers exhibited varied attrition patterns, with frequent travelers showing higher attrition rates.
By utilizing SQL queries and visualizations in Databricks, I explored the employee attrition data from multiple dimensions, uncovering important patterns. These insights are valuable for understanding the underlying causes of attrition within the organization.
The findings can now be used for further predictive modeling or strategic interventions aimed at reducing attrition and confirming assumption found. I will extend this analysis into a more advanced predictive model for attrition forecasting or decision-making using logistic regression, decision trees, and neural networks to find variables that contribute to attrition and what prevent attrition.