<p>Employee productivity is vital for modern corporate success, as highly productive employees perform better and have lower at-trition rates. Productivity enhances creativity, customer satisfac-tion, and cost efficiency. However, measuring the complex fac-tors driving innovation can be challenging, as traditional meth-ods often fail to capture the connections between human traits, workplace dynamics, and organizational culture.To address this, we developed a methodology using two open-source Kaggle datasets—one predicting employee frustration and the other predicting performance. Data preprocessing included numeri-cal conversion, feature scaling, and transforming variables into dummy variables to improve machine learning model perfor-mance. Several models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained and tested. We used 80% of the data for training and 20% for testing, with 5-fold cross-validation to prevent overfitting. Hy-perparameter tuning was applied to optimize the models, which were evaluated using precision, recall, F1-score, and accuracy. SVM and DT achieved 100% accuracy on both datasets, with SVM showing superior performance due to its effectiveness in handling high-dimensional classification problems.This struc-tured approach offers valuable insights into the relationships be-tween variables and outcomes, promoting consistency and re-producibility in machine learning projects.</p>