Study on postal life insurance attributes and its growth prediction using machine learning algorithms
Bibliographic record
Abstract
The oldest insurer in the country, since 1884, is Postal Insurance. For today's livelihood, the citizens of India's life-saving coverage and insurance have become necessary. For customers to overcome difficult situations, life insurance is crucial in creating confidence. This is one of the highlights of the Postal organization. Under postal life insurance (PLI), the volume of new policies is enrolled throughout India, and a supervised machine learning (ML) process for finding the business cluster is carried out based on this data, which is discussed. A ML algorithm that predicts the growth for the future, using a suitable algorithm for accessing the features and process to identify the prediction model, has been developed, which is the main goal of this study. Simulation results show that expected is one of the most important variables used to predict and that both random forest (RF) and logistic regression outperformed the other two models. The RF model is the most effective and fastest in predicting the system's future state, and it shows the highest value for the PLI product.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".