Python Project – Employee Productivity with Decision Tree

Project Goal: Classify whether productivity targets were met, helping decision-makers track and analyze team performance in the garment industry

Project Outcome: Built a decision tree classifier with training and test accuracies of 83.59% and 84.58%, respectively. The model identified key factors influencing productivity, achieving a test F1 score of 90.08%. Cross-validation showed reliable average performance, confirming model robustness

Project Methods: Performed data cleaning and determined which features to retain. Evaluated the decision tree model's effectiveness using various metrics and used K-fold cross-validation to validate them. Created a graphic that highlights the most effective factors for predicting team productivity and employed random forest to confirm the decision tree's performance