US Visa Approval Prediction
The visa approval process can feel like a guessing game—there are so many factors at play, and the waiting can be stressful. To help cut through the uncertainty, I built a visa approval prediction model. By analyzing historical data, this system gives applicants a clearer picture of their chances, long before they hit the “submit” button. It’s like getting a sneak peek at the outcome, helping both applicants and advisors make informed decisions with greater confidence. And best of all, it doesn’t involve any guesswork—just data-driven predictions you can rely on.
3 Months
ML, Predictive Analytics
Immigration, Legal Tech, Government
Challenge
Visa approval is influenced by numerous factors, making it difficult to predict approval chances. The challenge was to create a machine learning model capable of predicting US visa approval chances based on historical data and various applicant features.
Results
Developed a machine learning model to predict US visa approval chances with 89% accuracy. The model was deployed on AWS EC2 with an integrated retraining pipeline to keep the model up-to-date with the latest visa trends. This system helped streamline decision-making processes for visa applicants and advisors, providing near-instant predictions with a response time of 0.8 seconds per request, increasing speed by 50%.
89%
Model Accuracy
30%
Estimated Cost Savings through Automation
25%
Increase in Model Relevancy by automated retaining for every 30 days
Process
Data Analysis & Feature Engineering:
Collected and cleaned historical visa data, analyzing applicant features like age, education, job profile, and country of origin.
Applied feature engineering techniques to enhance model accuracy.
Model Development:
Built a classification model using XGBoost to handle complex relationships between features.
Applied hyperparameter tuning to optimize the model’s performance.
Deployment:
Deployed the model on AWS EC2 for scalability and availability.
Set up an auto-retraining pipeline using AWS S3 and AWS Lambda, ensuring the model remained up-to-date with recent visa data.
API Integration:
Exposed the model predictions via a Flask API, allowing users to submit requests and receive visa approval predictions instantly.
Conclusion
Through the application of machine learning and predictive modeling, the US Visa Approval Prediction system introduced a reliable, data-driven approach to visa application assessments. With the ability to predict outcomes in real-time and retrain the model continuously, the project illustrated the impact of data science in governmental and legal domains. It provided both applicants and advisors with actionable insights, streamlining decision-making in the complex visa process.