Data Validation for Diabetes 130 US Hospitals 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 Diabetes 130 US Hospitals dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The data set is the Diabetes 130-US Hospitals for years 1999-2008 donated to the University of California, Irvine (UCI) Machine Learning Repository. The dataset represents ten years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes.

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: Diabetes 130-US Hospitals for years 1999-2008 Dataset

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008

The HTML formatted report can be found here on GitHub.

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 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 Apr Data Set

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-apr-2021

The HTML formatted report can be found here on GitHub.

Data Validation for Kaggle Tabular Playground Series Mar 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 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.

Data Validation for Chicago Taxi Trips 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 Chicago Taxi Trips dataset is a regression situation where we attempt to predict the value of a continuous variable.

INTRODUCTION: The City of Chicago collects taxi trip data in its role as a regulatory agency. This example notebook illustrates how we can use TensorFlow Data Validation (TFDV) to investigate and visualize datasets. The data validation process includes examining descriptive statistics, inferring a schema, checking for and fixing anomalies, and detecting drift and skew in the dataset.

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: Chicago Taxi Trips Dataset, with modifications by TensorFlow.org

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/chicago_data.zip

The HTML formatted report can be found here on GitHub.