How ishvarya005/svm---insurancefraudclassification works
Purpose
This repository is a small, script-driven machine-learning project for insurance-claim data. Its only shared dataset is insurance_claims.csv; the Python scripts appear to implement SVM-based modeling variants or iterations.
Entry Points
Each Python file is an independent runnable entry point rather than part of a packaged application:
• svm_in_code.py
• svm_insurance3.py
• svm_insurance5.py
There is no README-defined command, service interface, package structure, or orchestration layer.
Data Boundary
insurance_claims.csv is the repository’s primary input boundary. The scripts are expected to load this tabular data, derive model-ready features and targets, and perform local training/evaluation. No separate raw/processed data directories or schema contracts are present in the tree.
Modeling Stage
The core computational concern is support-vector-machine modeling, indicated by all executable filenames:
• svm_in_code.py
• svm_insurance3.py
• svm_insurance5.py
These scripts likely represent alternative implementations, experiments, or successive revisions rather than composable pipeline modules. Compare their data preparation, target selection, SVM configuration, and evaluation behavior before selecting one as the canonical workflow.
Control Flow
The expected control flow is local and batch-oriented:
1. Invoke one SVM script.
2. Read insurance_claims.csv.
3. Transform insurance-record columns into model inputs.
4. Train and evaluate an SVM model.
5. Emit results through script-side output or artifacts.
There is no visible central dispatcher, reusable library layer, API, database, or asynchronous job system.
Runtime and Infrastructure
The material runtime is Python, via the three .py scripts. The repository does not show deployment infrastructure, container definitions, cloud resources, CI configuration, or external service integrations. Execution therefore depends on the local Python environment and whatever ML/data dependencies are imported by the scripts.
Architectural Implication
Treat the repository as an experimental analytics codebase, not a production service. The main engineering boundary is standardizing duplicated logic across svm_in_code.py, svm_insurance3.py, and svm_insurance5.py around a single input dataset, while preserving the chosen experiment’s preprocessing and model semantics.
Main entry points: insurance_claims.csv Insurance Claims CSV · svm_in_code.py SVM in Code · svm_insurance3.py SVM Insurance 3 · svm_insurance5.py SVM Insurance 5
Components 7
SVM Experiments
Independent local batch scripts.
- SVM in Code · Independent SVM modeling variant.
svm_in_code.py - SVM Insurance 3 · Independent SVM modeling variant.
svm_insurance3.py - SVM Insurance 5 · Independent SVM modeling variant.
svm_insurance5.py - Feature & Target Preparation · Transforms insurance columns into model-ready inputs.
- SVM Training · Fits an SVM using the selected experiment configuration.
Other components
- Insurance Claims CSV · Shared input boundary for all experiments.
insurance_claims.csv - Evaluation Output · Local metrics, results, or artifacts emitted by a script.
Connections 8
- Insurance Claims CSV → SVM in Code: read by
- Insurance Claims CSV → SVM Insurance 3: read by
- Insurance Claims CSV → SVM Insurance 5: read by
- SVM in Code → Feature & Target Preparation: implements
- SVM Insurance 3 → Feature & Target Preparation: implements
- SVM Insurance 5 → Feature & Target Preparation: implements
- Feature & Target Preparation → SVM Training: model inputs
- SVM Training → Evaluation Output: metrics/results