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 Mar 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 may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the amount of an insurance claim. Although the features are anonymized, they have properties relating to real-world features.
Additional Notes: I adapted this workflow from the TensorFlow Data Validation tutorial on TensorFlow.org (https://www.tensorflow.org/tfx/tutorials/data_validation/tfdv_basic). 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 Mar Data Set
Dataset ML Model: Binary classification with numerical and categorical attributes
Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-mar-2021
The HTML formatted report can be found here on GitHub.