Developed an ensemble ML classification model to predict U.S. visa case outcomes (Certified vs Denied) using applicant and employer attributes. Performed EDA, sampling, and model tuning (Random Forest, Gradient Boosting, XGBoost) to improve decision efficiency and identify key policy drivers like education, experience, and wage trends.
machine-learning random-forest numpy exploratory-data-analysis pandas seaborn xgboost classification matplotlib smote gradient-boosting feature-importance ensemble-methods google-colab ada-boost one-hot-encoding class-imbalance-handling recall-optimization randomized-search-cv
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Updated
Mar 31, 2026 - Jupyter Notebook