Regression Model for Diabetes 130-US Hospitals Using Python and XGBoost

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms 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.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 63.72%. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final accuracy score of 64.94%. When we processed the test dataset with the final model, the model achieved an accuracy score of 65.39%.

CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.

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-beta.ics.uci.edu/ml/datasets/296

One potential source of performance benchmarks: http://www.hindawi.com/journals/bmri/2014/781670/

The HTML formatted report can be found here on GitHub.

Regression Model for Diabetes 130-US Hospitals Using Python and Scikit-learn

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms 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.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 61.20% using the training dataset. We selected the Logistic Regression and Random Forest models to perform the tuning exercises. After a series of tuning trials, the refined Random Forest model processed the training dataset with a final accuracy score of 64.38%. When we processed the test dataset with the final model, the model achieved an accuracy score of 64.61%.

CONCLUSION: In this iteration, the Random Forest model appeared to be a suitable algorithm for modeling this dataset.

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-beta.ics.uci.edu/ml/datasets/296

One potential source of performance benchmarks: http://www.hindawi.com/journals/bmri/2014/781670/

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.

Regression Model for Kaggle Tabular Playground Series 2021 Apr Using Python and AutoKeras

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground 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 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.

ANALYSIS: The performance of the cross-validated TensorFlow models achieved an average accuracy benchmark of 0.7702 after running for 45 trials. When we applied the final model to Kaggle’s test dataset, the model achieved an accuracy score of 0.7865.

CONCLUSION: In this iteration, the AutoKeras-generated TensorFlow model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Apr Data Set

Dataset ML Model: Regression with numerical and categorical attributes

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

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-apr-2021/leaderboard

The HTML formatted report can be found here on GitHub.

Regression Model for Kaggle Tabular Playground Series 2021 Apr Using Python and TensorFlow

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground 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.

ANALYSIS: The performance of the cross validated TensorFlow models achieved an average accuracy benchmark of 0.7689 after running for 15 epochs. When we applied the final model to Kaggle’s test dataset, the model achieved an accuracy score of 0.7831.

CONCLUSION: In this iteration, the TensorFlow model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Apr Data Set

Dataset ML Model: Regression with numerical and categorical attributes

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

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-apr-2021/leaderboard

The HTML formatted report can be found here on GitHub.

Regression Model for Kaggle Tabular Playground Series 2021 Apr Using Python and XGBoost

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground 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.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 0.7707. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final accuracy score of 0.7725. When we applied the last model to Kaggle’s test dataset, the model achieved a ROC score of 0.7832.

CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Apr Data Set

Dataset ML Model: Regression with numerical and categorical attributes

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

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-apr-2021/leaderboard

The HTML formatted report can be found here on GitHub.

Regression Model for Kaggle Tabular Playground Series 2021 Apr Using Python and Scikit-learn

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground 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.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 0.7253 using the training dataset. We selected k-Nearest Neighbors and Random Forest to perform the tuning exercises. After a series of tuning trials, the refined k-Nearest Neighbors model processed the training dataset with a final accuracy score of 0.7699. When we processed Kaggle’s test dataset with the final model, the model achieved an accuracy score of 0.7780.

CONCLUSION: In this iteration, the k-Nearest Neighbors model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Apr Data Set

Dataset ML Model: Regression with numerical and categorical attributes

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

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-apr-2021/leaderboard

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.