This project focuses on building a predictive model to determine the likelihood of visa approval based on candidate and job-related features. The goal is to assist in automating parts of the visa evaluation process by leveraging data-driven insights and machine learning models. Through exploratory data analysis (EDA), feature engineering, and model tuning, this project demonstrates the workflow of developing an end-to-end classification model.
- Understand key factors influencing visa approvals.
- Perform in-depth univariate and bivariate analysis of applicant and job attributes.
- Apply data preprocessing, feature selection, and model tuning.
- Compare different classification algorithms and evaluate their performance.
- Identify the most significant predictors contributing to visa approval.
- Data Preprocessing: Handling missing values, encoding categorical variables, and winsorizing outliers.
- Exploratory Data Analysis (EDA): Univariate and bivariate analyses to uncover variable distributions and relationships.
- Feature Engineering: Transformation and selection based on importance scores.
- Model Development: Logistic Regression, Random Forest, and Gradient Boosting (before and after hyperparameter tuning).
- Evaluation Metrics: F1-score, precision, recall, and accuracy.
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- Achieved the highest F1 Score (82.06%), matching Gradient Boosting but with easier interpretability.
- Handles class imbalance effectively.
- Reduces overfitting by aggregating multiple diverse decision trees.
- Robust to noise and scalable to high-dimensional data.
- Offers faster training and easier tuning than Gradient Boosting.
- Classifies visa petitions as likely to be certified or denied, helping prioritize workload and improve processing efficiency.
- Automatically flags borderline or uncertain cases for manual verification by legal teams, minimizing decision errors.
- Feature importance visualizations explain the reasons behind predictions, enhancing trust and transparency for stakeholders and applicants.
- Helps discover patterns (e.g., wage, experience, education) that impact certification rates, guiding internal policy and strategy decisions.
The Tuned Random Forest Classifier combines:
- Strong predictive performance
- Excellent interpretability
- Speed and scalability
- Ease of deployment
💡 Conclusion:
The Tuned Random Forest model proved to be the most practical and effective solution for EasyVisa’s automated visa prediction system, balancing accuracy, explainability, and operational efficiency.
- Python
- Pandas, NumPy for data manipulation
- Matplotlib, Seaborn for visualization
- Scikit-learn for model building and evaluation
- Jupyter Notebook for analysis workflow
1. Clone the repository:
git clone https://github.com/indu-explores-data/Visa-Approval-Prediction-using-ML/.git
cd Visa-Approval-Prediction-using-ML2. Install dependencies:
pip install -r requirements.txt
3. Open the Jupyter Notebook:
jupyter notebook "Visa Approval Prediction using ML.ipynb"
- Run each cell in the notebook sequentially to reproduce the analysis.
- Modify parameters (e.g., test size, model hyperparameters) to experiment with model behavior.
- Visualizations are auto-generated during execution for interactive exploration.
Let’s connect on LinkedIn for project discussions or data-driven collaborations:
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