EasyVisa · Ensemble Techniques
Professor Review
A rubric-aligned executive summary of the OFLC visa certification classification project, with evidence links to every page that produced each result.
Download submission deck
Enter the access code to download the full presentation.
Trains a GBM (50 trees) on 5,000 stratified rows — ~5–15 seconds.
Rubric coverage
01
Exploratory Data Analysis
Univariate, bivariate certification rates, distribution charts.
View evidence
02
Data Preprocessing
Anomaly clipping, wage annualization, one-hot encoding, stratified split.
View evidence
03
Model Building — 5+ models
Decision Tree, Bagging, Random Forest, AdaBoost, Gradient Boosting.
View evidence
04
Resampling comparison
15 runs across Original, Oversampled, Undersampled variants.
View evidence
05
Hyperparameter tuning
Live sliders for n_estimators, depth, leaf, and learning rate.
View evidence
06
Final model selection
Chosen GBM with confusion matrix, ROC, feature importance.
View evidence
07
Insights & Recommendations
Tiered decision policy with governance and honest limitations.
View evidence
Evaluator Review
Evaluator review workflow
Enter the review access code to capture evaluator notes and store them as a numbered version alongside this submission.