Data Analytics for Improving Employee Retention in the U.S. Technology Sector

Employee retention is growing important in the U.S. technology industry, where continuous turnover disturbs output, innovation, and team chemistry. This work uses Stack Overflow Developer Survey data to investigate and project staff retention trends using a hybrid machine learning approach combining Principal Component Analysis (PCA) with XGBoost. We lower the dimensionality of retention-related data using PCA, separating essential elements such as compensation, work-life balance, career growth, and remote work flexibility, which tremendously influence employees' choices to stay with their companies. Using these enhanced components, XGBoost predicts retention risk with higher interpretability and accuracy. Based on this, our proposed model exhibits a balanced capacity to identify retained and at-risk personnel by obtaining good performance metrics with a 90% accuracy rate, 0.91 precision, and 0.98 recall. These outcomes surpass baseline models, including Random Forest (RF) and Support Vector Classifier (SVC), proving our PCA-XGBoost method's effectiveness in prediction accuracy. The results reveal how decisions concerning remote work, chances for professional growth, and competitive compensation scales affect retention. This paper provides HR teams in IT companies with a helpful tool for data-driven retention strategies through a scalable and interpretable framework, boosting long-term workforce stability and tackling important retention issues in the always-changing tech environment.