EasyVisa is a machine learning project that predicts U.S. visa approval outcomes using immigration application data. The system analyzes historical visa records and identifies patterns that influence approval decisions.
First, I explored the visa application dataset and cleaned the data to prepare it for modeling. Then, I engineered predictive features that capture employer characteristics, job requirements, and application details.
Next, I trained several machine learning classification models to predict whether a visa application will be approved or denied.
In addition, I evaluated the models using appropriate performance metrics and compared their results to identify the best approach.
Overall, the EasyVisa project demonstrates how machine learning can analyze immigration datasets and generate insights about visa approval outcomes.