Employee attrition poses significant financial and operational challenges for organizations, necessitating a deeper understanding of its drivers and associated factors. This study leverages a fictional IBM dataset containing employee attributes—such as age, education, job role, monthly income, and job satisfaction—to develop predictive models for attrition (binary classification) and monthly income (regression). Employing techniques including logistic regression, decision trees, random forests, and ensemble methods, the analysis involved feature engineering, data cleaning, and model evaluation using metrics like AUC for attrition and confidence intervals for income predictions. The results identified job satisfaction, years at the company, and monthly income as the primary predictors of attrition, while job level, job role, and working years were key determinants of monthly income. These findings demonstrate the efficacy of machine learning in enhancing prediction accuracy, offering actionable insights for companies to mitigate attrition and assess salary alignment, despite the exclusion of advanced methods like gradient boosting and neural networks.