Portfolio Details

Project information

Category: Predictive analytics, Machine Learning, Dashboard, Python, Streamlit, Tableau, Random Forest, Logistic Regression, AdaBoost, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), GridSearchCV
Project date: May 2025
Project URL: Github | Streamlit | Tableau

Developed a machine learning system to predict student dropouts and support early intervention. Applied models including Random Forest, Logistic Regression, AdaBoost, LDA, and QDA, with hyperparameter tuning via GridSearchCV. Built interactive dashboards using Streamlit and Tableau to visualize risk levels and key insights, enabling data-driven educational strategies.