Great Expectations, a data validation library for Python
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Improve data quality and enhance machine learning models with Great Expectations, a Python-based open-source library. Validate, document, and profile your data for better insights and results. Learn how to explore data using Great Expectations in this project.
At a Glance
Garbage in, garbage out but sometimes gold could be wrongly put in the garbage. When data scientists are doing projects, the dataset-machine learning model pipeline requires appropriate data formats. But how could we check that our datasets are in good shape before our modelling? Great Expectations is the perfect tool for it. In this project, you could learn how to do data exploration using Great Expectations. Great Expectations is a Python-based open-source library for validating, documenting, and profiling your data. It helps you to maintain data quality and improve models.
Great Expectations is a Python-based open-source library for validating, documenting, and profiling your data. It helps you to maintain data quality and improve the usage of the data used in machine learning models. Mostly, Data science and data engineering teams use Great Expectations to:
Here, in this project, we are going to show the basic usage of Great Expectations and apply it to an example bank churn data, modified from the one provided by Kaggle.
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