Multi-Class Model for Kaggle Tabular Playground Series 2021 December Using AutoKeras

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

SUMMARY: The 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 Series December 2021 dataset is a multi-class modeling situation where we are trying to predict one of several (more than 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 a real dataset and generated using a CTGAN. The dataset is used for this competition is synthetic but based on a real dataset and generated using a CTGAN. This dataset is based on the original Forest Cover Type Prediction competition.

ANALYSIS: After a series of tuning trials, the best AutoKeras model processed the training dataset with an accuracy score of 72.54%. When we processed the test dataset with the final model, the model achieved an accuracy score of 70.71%.

CONCLUSION: In this iteration, the AutoKeras model did not appear to be a suitable algorithm for modeling this dataset without using additional trial iterations.

Dataset Used: Kaggle Tabular Playground Series December 2021 Data Set

Dataset ML Model: Multi-Class classification with numerical and categorical attributes

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

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

The HTML formatted report can be found here on GitHub.

Binary-Class Model for Kaggle Tabular Playground Series 2021 November Using AutoKeras

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

SUMMARY: The 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 November 2021 dataset is a binary classification situation where we are trying 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 a real dataset and generated using a CTGAN. The data is synthetically generated by a GAN trained on a real-world dataset used to identify spam emails via various extracted features from the email. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: After a series of tuning trials, the best AutoKeras model processed the training dataset with a ROC/AUC score of 0.7492. When we processed the test dataset with the final model, the model achieved a ROC/AUC score of 0.7484.

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

Dataset Used: Kaggle Tabular Playground 2021 November Data Set

Dataset ML Model: Binary classification with numerical attributes

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

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

The HTML formatted report can be found here on GitHub.

Binary-Class Model for Kaggle Tabular Playground Series 2021 October Using 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 October 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 a real dataset and generated using a CTGAN. The original dataset deals with predicting the biological response of molecules given various chemical properties. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: After a series of tuning trials, the best AutoKeras model processed the training dataset with a ROC/AUC score of 0.8431. When we processed the test dataset with the final model, the model achieved a ROC/AUC score of 0.8441.

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

Dataset Used: Kaggle Tabular Playground 2021 October Data Set

Dataset ML Model: Binary classification with numerical and categorical attributes

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

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

The HTML formatted report can be found here on GitHub.

Binary-Class Model for Kaggle Tabular Playground Series 2021 September Using AutoKeras

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

SUMMARY: The 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 Series September 2021 dataset is a binary classification situation where we are trying to predict one of the two possible outcomes.

INTRODUCTION: 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: After a series of tuning trials, the best AutoKeras model processed the training dataset with a ROC/AUC score of 0.5907. When we processed the test dataset with the final model, the model achieved a ROC/AUC score of 0.5932.

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

Dataset Used: Kaggle Tabular Playground 2021 September Data Set

Dataset ML Model: Binary classification with numerical and categorical attributes

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

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

The HTML formatted report can be found here on GitHub.

Multi-Class Model for Kaggle Tabular Playground Series 2021 June Using 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 June 2021 dataset is a multi-class modeling situation where we attempt to predict one of several (more than 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 a real dataset and generated using a CTGAN. The original dataset deals with predicting the category on an eCommerce product given various attributes about the listing. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: After a series of tuning trials, the best AutoKeras model processed the training dataset with a logarithmic loss of 1.7691. When we processed the test dataset with the final model, the model achieved a logarithmic loss of 1.7686.

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

Dataset Used: Kaggle Tabular Playground 2021 June Data Set

Dataset ML Model: Multi-Class classification with numeric attributes

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

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

The HTML formatted report can be found here on GitHub.

Multi-Class Model for Kaggle Tabular Playground Series 2021 May Using 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 May 2021 dataset is a multi-class modeling situation where we attempt to predict one of several (more than 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 a real dataset and generated using a CTGAN. The original dataset deals with predicting the category on an eCommerce product given various attributes about the listing. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: After a series of tuning trials, the best AutoKeras model processed the training dataset with a logarithmic loss of 1.0984. When we processed the test dataset with the final model, the model achieved a logarithmic loss of 1.1023.

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

Dataset Used: Kaggle Tabular Playground 2021 May Data Set

Dataset ML Model: Multi-Class classification with categorical attributes

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

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

The HTML formatted report can be found here on GitHub.

Binary Classification Model for Kaggle Tabular Playground Series 2021 March Using 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 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.

ANALYSIS: After a series of tuning trials, the best AutoKeras model processed the training dataset with a ROC/AUC score of 88.06%. When we processed the test dataset with the final model, the model achieved a ROC/AUC score of 87.82%.

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

Dataset Used: Kaggle Tabular Playground 2021 March Data Set

Dataset ML Model: Binary classification with numerical and categorical attributes

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

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

The HTML formatted report can be found here on GitHub.

Tabular Data Analytics Project Templates Using Python and AutoKeras Version 3

As I work on practicing and solving machine learning (ML) problems, I find myself repeating a set of steps and activities repeatedly.

Thanks to Dr. Jason Brownlee’s suggestions on creating a machine learning template, I have pulled together a project template that I use to experiment with modeling ML problems using Python and the AutoKeras library.

Version 3 of the AutoKeras templates contain updated structures and code like the previous templates. I designed the templates to address regression, binary classification, and multi-class classification modeling exercises from beginning to end.

You will find the Python templates on the Analytics Project Templates page.