Overview
EasyVisa is a predictive modeling solution for visa applicant outcomes. Using ensemble learning techniques (bagging, boosting, and stacking), the system predicts whether a visa application will be approved or denied, and identifies the key factors that drive these decisions.
Business Problem
Visa processing involves complex decision-making with many variables. Understanding which factors most influence approval decisions can help applicants improve their chances and help processing agencies identify applications that need additional scrutiny.
Approach
- Data: Historical visa application data with outcomes (approved/denied).
- Models: Multiple ensemble methods compared: Bagging (Random Forest, Bagged Decision Trees), Boosting (Gradient Boosting, AdaBoost, XGBoost), and Stacking (Meta-learner combining best individual models).
- Feature Importance: Identified key decision factors driving visa outcomes.
- Business Recommendations: Translated model insights into actionable guidance.
Key Techniques
- Ensemble learning (bagging, boosting, stacking)
- Feature importance analysis
- Model comparison and selection
- Business translation of ML insights
Tech Stack
Bagging Boosting Stacking