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Datasets with the same probabilistic distribution

The goal of synthetic data is to create a dataset that is as realistic as possible, and does so without endangering important pieces of personal information. For instance, a team at Deloitte Consulting generated 80% of the training data for a machine learning model by synthesizing data. The resulting model accuracy was similar to a model trained on real data.

In this exercise, you will generate a synthetic dataset from scratch using Faker that follows a probabilistic distribution loaded as p.

The Faker generator fake_data has been already initialized and numpy is imported as np.

This exercise is part of the course

Data Privacy and Anonymization in Python

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Obtain or specify the probabilities
p = (0.46, 0.26, 0.16, 0.1, 0.02)

# Generate 5 random cities 
cities = ____

# See the generated cities
print(cities)
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