Exercise

Consistent synthetic dataset

One scenario in which companies use synthetic data is the training of artificial intelligence and machine learning models. Real-world data is sometimes expensive to collect, or simply hard to come by. When the training data is highly imbalanced (e.g., more than 90% of instances belong to one class), synthetic data generation can help build accurate machine learning models.

In this exercise, you will generate a mobile app rating dataset using Faker.

The initial DataFrame is loaded as ratings with two columns: rating and gender. A Faker() generator has already been initialized for you as fake_data.

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Create a name column in the ratings DataFrame containing unique names corresponding to the gender column.