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Bankruptcy Prediction Scoring Model
Project type
Prediction
Tools
Python, Scikit-learn, Flask/Django, MLflow, Docker, AWS, Git
Skills
Predictive modeling, Feature importance analysis, API development, MLOps implementation, Experiment tracking, Data drift analysis, Containerization
Developed a scoring model to predict the probability of client bankruptcy, automating the assessment process while providing feature importance analysis to enhance transparency. Built and trained a predictive model to estimate bankruptcy risks, incorporating both global feature importance (to identify the most influential variables overall) and local feature importance (to explain predictions at an individual client level).
Deployed the model as a production-ready API, accompanied by a user-friendly testing interface for API validation. Implemented a comprehensive MLOps framework for end-to-end monitoring and management, including experiment tracking using MLflow, and data drift analysis to maintain model performance over time. Utilized tools such as Docker for containerization and cloud services for scalable deployment, ensuring robustness and reliability in production.
