ahmedunshur/synthetic-data-generation-pycon-somalia-2025
Type de record:
project
Créateur:
ahm
Hôte:
Demo project for the PyCon Somalia 2025 talk “Synthetic Data Generation with Python and LLMs”.
# Synthetic Data Generation with Python and LLMs
Synthetic data refers to artificially generated data that mimics the statistical properties and patterns of real-world data, but without containing any original identifying information. It is primarily used in scenarios where real data is sensitive, scarce, or difficult to access due to privacy concerns, regulations, or logistical challenges.
## Overview
This repository contains the demo code for the talk "Synthetic Data Generation with Python and LLMs," presented at PyCon Somalia 2025.
The project demonstrates two approaches to synthetic data generation, using diabetes patient records as an example:
1. Using statistical methods to generate synthetic data with NumPy. See `demo_1_using_numpy.ipynb`.
2. Using generative models and LLMs with LangChain and OpenAI models. See `demo_2_using_llm.ipynb`.
## Installation
Requires Python 3.10+ and uv.
```bash
git clone github.com
cd synthetic-data-generation-pycon-somalia-2025
uv sync
```
## Configuration
Set the `OPENAI_API_KEY` environment variable which is required for OpenAI models.
This will be used in `demo_2_using_llm.ipynb` notebook.
**SECURITY NOTE:** Make sure `.env` is NOT committed to version control. Add it to `.gitignore`.
## Dataset Description
The synthetic diabetes patient datasets contain the following fields:
| Field Name | Description |
|------------|-------------|
| `Patient_ID` | Unique patient identifier |
| `Date_of_birth` | Patient's date of birth in YYYY-MM-DD format |
| `Sex` | Patient's biological sex (Male, Female) |
| `Diabetes_type` | Type of diabetes (Type 1, Type 2) |
| `HbA1c_percent` | Glycated hemoglobin level, indicates blood glucose control over 2-3 months |
| `Fasting_Glucose_mg_dL` | Fasting blood glucose (mg/dL) |
| `BMI_kg_m2` | Body Mass Index (kg/m²) |
| `Last_Visit_Date` | Most recent clinical visit date (within last 6 months) in YYYY-MM-DD format |
# …