Part-2 Building a Synthetic Tabular Data Generator With GPT-4 and Python

Part 2 of blog series is a deep, code-heavy guide that walks you readers through building and validating a synthetic data generator using GPT-4 + Python. in Part 1 we have already learned the importance of Synthetic data and why it is needed. If you have not read Part 1 yet, Click Here

Goal:

In this section, our objective is to use GPT-4 to generate realistic tabular datasets tailored to specific schema and business logic, then validate the synthetic data using statistical checks and model performance comparisons.

Why Do This?

Real-world data is:

Hard to get
Often dirty
Expensive to label
Biased or imbalanced

Synthetic data gives you a sandbox for rapid model prototyping without needing real Personally Identifiable Information(PII) or waiting for collection cycles.

Outline

1. Define the Schema and Business Logic

Start with a schema for a dataset you would actually use. For example: loan approval data.

schema = {
    "age": "integer between 18 and 70",
    "income": "float, 20,000 - 150,000 USD",
    "credit_score": "integer, 300 to 850",
    "loan_amount": "float, 5,000 - 100,000 USD",
    "approved": "binary, 1 if loan is approved, 0 otherwise"
}

We shall prompt GPT to generate rows based on this schema, but also embed rules:

“If income < 30,000 and credit_score < 600, then likely not approved.”

2. Use GPT-4 to Generate the Synthetic Data

You can run this through OpenAI’s Python API or interactively in ChatGPT Code Interpreter:

import openai

openai.api_key = "your-api-key"

prompt = """
Generate 100 rows of loan application data in JSON format.
Each row should have: age (18-70), income (20k-150k), credit_score (300-850), loan_amount (5k-100k), and approved (0 or 1).

Rules:
- If income < 30k and credit_score < 600, approved should be 0.
- If income > 100k and credit_score > 700, approved is likely 1.
"""

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": prompt}],
    temperature=0.7
)

import json
data_json = json.loads(response['choices'][0]['message']['content'])

Tip: Ask GPT to return the data as a CSV block or structured list for easier parsing.

3. Convert and Inspect with Pandas

import pandas as pd

df = pd.DataFrame(data_json)

print(df.head())
print(df.describe())

Check for:

Range adherence
Missing values
Logical rule compliance

4. Validate the Data

Use quick plots and statistical checks:

import seaborn as sns
import matplotlib.pyplot as plt

sns.pairplot(df, hue="approved")
plt.show()

# Correlation matrix
print(df.corr())

Check if:

Distributions match expectations
Approval logic roughly holds
There’s no obvious leakage

5. Train a Quick Classifier to Benchmark Quality

Train a model on this synthetic data:

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

X = df.drop("approved", axis=1)
y = df["approved"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

clf = RandomForestClassifier()
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))

Compare performance to a model trained on real data (if available). You’re testing how “trainable” your synthetic data really is.

6. Optional: Mix Synthetic + Real Data

Use GPT-generated examples to balance classes or fill data gaps. Fine-tune a model trained on real data using synthetic boosters for rare conditions.

What to Watch Out For

Overfitting to synthetic logic: Models trained purely on synthetic data may memorize patterns that don’t generalize.
Inconsistent rule application: GPT can drift—validate with logic checks.
Hidden correlations: Synthetic data may introduce spurious relationships unless carefully guided.