Case StudyUMKM Decision Support
Predictive analytics and decision support for MSMEs.
A data-driven system analyzing UMKM datasets with multiple ML algorithms to generate predictive insights for small business decision-making.
Collaborators
Overview
Turning MSME data into actionable business intelligence.
Evaluated SVM, Random Forest, XGBoost, and Logistic Regression — achieving 91% peak accuracy. Built interactive visualizations with Matplotlib and Seaborn, deployed via Flask with MySQL persistence.
Outcome
- 91% accuracy with Logistic Regression (vs SVM, Random Forest, XGBoost)
- Interactive dashboards for MSME pattern analysis
- Flask-based decision support system