Responsible AI: Principles and Practices/

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Case Study: Synthetic Data Generation

Case Study: Synthetic Data Generation

Learn how to use the Faker library to generate synthetic data for business applications.

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Synthetic data generation is a crucial skill in various fields, including data science, machine learning, and software testing. By creating realistic yet artificial datasets, we can overcome data privacy concerns, facilitate model development, and enhance the robustness of our applications.

In this hands-on example, you’ll learn how to use the Faker library to generate synthetic datasets with diverse characteristics.

Getting started with generating synthetic data with Faker

Faker is a Python library that enables us to generate synthetic but realistic data for various purposes. With Faker, we can easily create synthetic data such as names, addresses, emails, and more, which closely resemble real-world data.

In the following code snippet, we’ll demonstrate how to use Faker to generate sample data for a hypothetical customer database.

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# Import library
from faker import Faker
import pandas as pd # for data manipulation
# Instantiate Faker() instance
fake = Faker()
# Create a dataset including names, addresses, emails, and phone numbers
data = []
for _ in range(10):
    data.append([fake.name(), fake.address(), fake.email(), fake.phone_number()])
# Convert to DataFrame
df = pd.DataFrame(data, columns=['Name', 'Address', 'Email', 'Phone'])
print(df)

The code explanation is given below:

  • Lines 1–3: We import the Faker and pd library.

  • Lines 5–6: We initialize an instance of ...