Data Validation for Kaggle Tabular Playground Series Apr 2021 Using Python and TensorFlow Data Validation

SUMMARY: The project aims to construct a data validation flow using TensorFlow Data Validation (TFDV) and document the end-to-end steps using a template. The Kaggle Tabular Playground Series Apr 2021 dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. The dataset used for this competition is synthetic but based on the real Titanic dataset and generated using a CTGAN. The statistical properties of this dataset are very similar to the original Titanic dataset, but there is no shortcut to cheat by using public labels for predictions.

Additional Notes: I adapted this workflow from the TensorFlow Data Validation tutorial on ( I also plan to build a TFDV script for validating future datasets and building machine learning models.

CONCLUSION: In this iteration, the data validation workflow helped to validate the features and structures of the training, validation, and test datasets. The workflow also generated statistics over different slices of data which can help track model and anomaly metrics.

Dataset Used: Kaggle Tabular Playground 2021 Apr Data Set

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference:

The HTML formatted report can be found here on GitHub.