Project Overview
This project involved designing and implementing a data science pipeline to address inefficiencies in healthcare claim processing. By analyzing historical claims data, the project aimed to predict claim denials and optimize financial forecasting for healthcare providers. Advanced machine learning models and statistical techniques were employed to ensure actionable insights.
Key Outcomes
- Achieved 85% accuracy in predicting claim denials, enabling better resource allocation.
- Implemented SARIMA models to improve financial forecasting by reducing prediction errors by 20%.
- Provided actionable insights for healthcare stakeholders to mitigate financial risks.
Tools and Techniques Used
- Random Forest, SARIMA, Logistic Regression
- Python, Pandas, NumPy, Scikit-learn
- Data preprocessing and visualization
Download the Final Report
Click the link below to download the detailed final project report:
Download Final Report
