Visual Prototype: Demonstrating Workflow and Impact
To classify EPFO (Employees' Provident Fund Organisation) data, such as member records and transaction details, with limited labeled examples. This will be achieved by implementing and evaluating semi-supervised learning models to leverage both labeled and unlabeled data effectively.
Source: EPFO Member Records, Transaction Data.
Total Records: ~2,500,000 (Illustrative)
Key Features: Member ID, Age, Contribution Amount, Transaction Type, Last Active Date, Employer ID, Claim Type, etc.
Goal: Leverage the 85% unlabeled data for robust classification.
Confidence Threshold (for Pseudo-Labeling): 0.95
Max Iterations:
Base Classifier: LightGBM / XGBoost
Newly Classified Records: --
Overall Accuracy (on test set): --
Avg. Confidence (New Labels): --
| Record ID | Predicted Category | Confidence |
|---|---|---|
| Awaiting classification... | ||
Achieves high accuracy by effectively leveraging the vast amounts of unlabeled EPFO data alongside limited labeled examples.
Significantly minimizes the time, cost, and human resources required for manual data labeling, making the process more scalable.
Improves overall data quality, consistency, and usability, leading to better insights, policy formulation, and operational efficiency for EPFO.