Tag: data validation

Data Validation for Bondora P2P Lending 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 Bondora P2P Lending dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The Kaggle dataset owner retrieved this dataset from Bondora, a leading European peer-to-peer lending platform. The data comprises demographic and financial information of the borrowers with defaulted and non-defaulted loans between February 2009 and July 2021. For investors, “peer-to-peer lending” or “P2P” offers an attractive way to diversify portfolios and enhance long-term performance. However, to make effective decisions, investors want to minimize the risk of default of each lending decision and realize the return that compensates for the risk. Therefore, we will predict the default risk by focusing on the “DefaultDate” attribute as the target.

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). The plan is to build a robust TFDV script for validating datasets in 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 Bondora P2P Lending Loan Data

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://www.kaggle.com/sid321axn/bondora-peer-to-peer-lending-loan-data

Dataset Attribute Description: https://www.bondora.com/en/public-reports

The HTML formatted report can be found here on GitHub.

Data Validation for LendingClub Loan Data 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 LendingClub Loan Data dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The Kaggle dataset owner derived this dataset from the publicly available data of LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). An investor naturally would want to invest in people who showed a profile of having a high probability of paying back the loan. The dataset uses the lending data from 2007 to 2010, and we will try to predict whether the borrower paid back their loan in full.

Additional Notes: I adapted this workflow from the TensorFlow Data Validation tutorial on TensorFlow.org. The plan is to build a robust TFDV script for validating datasets in 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 LendingClub Loan Data

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://www.kaggle.com/itssuru/loan-data

The HTML formatted report can be found here on GitHub.

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.