> ## Documentation Index
> Fetch the complete documentation index at: https://docs.verisynth.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Generate your first synthetic dataset with VeriSynth Core.

# Quickstart

Let’s generate your first **verifiable synthetic dataset** — right from your terminal.

VeriSynth runs 100% locally, needs **no GPU**, and produces both a **synthetic dataset** and a **cryptographic proof receipt** for auditability.

***

## 1. Install VeriSynth Core

```bash theme={null}
pip install verisynth-core
```

That’s all you need. VeriSynth ships as a self-contained Python package with zero external dependencies beyond standard data libraries.

***

## 2. Prepare your dataset

Start with any CSV file — for example, a small tabular dataset of patients, transactions, or IoT readings.

```bash theme={null}
data/patients.csv
```

Example data:

| patient\_id | age | bmi  | smoker | hba1c | systolic\_bp | diastolic\_bp |
| ----------- | --- | ---- | ------ | ----- | ------------ | ------------- |
| 001         | 62  | 31.4 | 1      | 7.8   | 148          | 90            |
| 002         | 45  | 28.6 | 0      | 6.1   | 132          | 82            |
| 003         | 33  | 24.1 | 0      | 5.3   | 116          | 74            |

You can also grab sample data here: [https://github.com/VeriSynthAI/verisynth-core/tree/main/data](https://github.com/VeriSynthAI/verisynth-core/tree/main/data)

***

## 3. Generate synthetic data

Run VeriSynth directly from the CLI:

```bash theme={null}
verisynth data/patients.csv -o out/ --rows 1000000 --seed 42
```

✅ This will produce two files:

```
📁 out/synthetic.csv   # Synthetic dataset
🧾 out/proof.json      # Cryptographic proof receipt
```

***

## 4. Inspect the proof receipt

Each run creates a verifiable JSON proof describing:

* Dataset hashes (SHA-256)
* Model configuration (Gaussian Copula)
* Random seed (for reproducibility)
* Privacy and correlation metrics
* Merkle root for integrity checking

Example:

```json theme={null}
{
  "verisynth_version": "core-0.1.0",
  "metrics": { "corr_mean_abs_delta": 0.12, "naive_reid_risk": 0.01 },
  "input":  { "rows": 10, "sha256": "…82b7" },
  "output": { "rows": 1000000, "sha256": "…acb9" },
  "proof": "merkle_root: …c31"
}
```

***

## 5. Explore your synthetic dataset

The generated data preserves the **statistical structure** of your original dataset — correlations, distributions, and realistic relationships — without containing any real records.

Try viewing it in pandas:

```python theme={null}
import pandas as pd
df = pd.read_csv("out/synthetic.csv")
df.head()
```

***

## 6. Reproduce your results

Because VeriSynth uses deterministic seeding (`--seed`), you can always reproduce the exact same synthetic dataset and proof receipt from the same input file.

```bash theme={null}
verisynth data/patients.csv -o out/ --rows 1000000 --seed 42
```

Running this command again, with the same `seed` value will yield **identical Merkle roots and metrics**, proving the process is verifiable and consistent.

***