# ishvarya005/svm---insurancefraudclassification architecture

> Architecture diagram of the GitHub repository ishvarya005/svm---insurancefraudclassification, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-08-14). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/ishvarya005/svm---insurancefraudclassification
- Repository: https://github.com/ishvarya005/svm---insurancefraudclassification

## Overview

#### 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`](https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/insurance_claims.csv) (Insurance Claims CSV), [`svm_in_code.py`](https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/svm_in_code.py) (SVM in Code), [`svm_insurance3.py`](https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/svm_insurance3.py) (SVM Insurance 3), [`svm_insurance5.py`](https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/svm_insurance5.py) (SVM Insurance 5)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_experiments["SVM Experiments"]
  node_svm_in_code["SVM in Code<br/>Python entry point<br/>[svm_in_code.py]"]
  node_svm_insurance3["SVM Insurance 3<br/>Python entry point<br/>[svm_insurance3.py]"]
  node_svm_insurance5["SVM Insurance 5<br/>Python entry point<br/>[svm_insurance5.py]"]
  node_feature_target_prep["Feature &amp; Target Preparation<br/>local pipeline stage"]
  node_svm_training["SVM Training<br/>modeling stage"]
end

node_dataset[("Insurance Claims CSV<br/>tabular dataset")]
node_evaluation_output["Evaluation Output<br/>script output"]

node_dataset -->|"read by"| node_svm_in_code
node_dataset -->|"read by"| node_svm_insurance3
node_dataset -->|"read by"| node_svm_insurance5
node_svm_in_code -.->|"implements"| node_feature_target_prep
node_svm_insurance3 -.->|"implements"| node_feature_target_prep
node_svm_insurance5 -.->|"implements"| node_feature_target_prep
node_feature_target_prep -->|"model inputs"| node_svm_training
node_svm_training -->|"metrics/results"| node_evaluation_output

click node_dataset "https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/insurance_claims.csv"
click node_svm_in_code "https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/svm_in_code.py"
click node_svm_insurance3 "https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/svm_insurance3.py"
click node_svm_insurance5 "https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/svm_insurance5.py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_svm_in_code,node_svm_insurance3,node_svm_insurance5,node_feature_target_prep,node_svm_training toneBlue
class node_dataset,node_evaluation_output toneNeutral
```

## Components

### SVM Experiments

Independent local batch scripts.

- [SVM in Code](https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/svm_in_code.py): Python entry point · Independent SVM modeling variant. · `svm_in_code.py`
- [SVM Insurance 3](https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/svm_insurance3.py): Python entry point · Independent SVM modeling variant. · `svm_insurance3.py`
- [SVM Insurance 5](https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/svm_insurance5.py): Python entry point · Independent SVM modeling variant. · `svm_insurance5.py`
- **Feature & Target Preparation**: local pipeline stage · Transforms insurance columns into model-ready inputs.
- **SVM Training**: modeling stage · Fits an SVM using the selected experiment configuration.

### Other components

- [Insurance Claims CSV](https://github.com/ishvarya005/svm---insurancefraudclassification/blob/master/insurance_claims.csv): tabular dataset · Shared input boundary for all experiments. · `insurance_claims.csv`
- **Evaluation Output**: script output · Local metrics, results, or artifacts emitted by a script.

## Connections

- 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