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.

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.

Binary Class Image Classification Analytics Project Template Using TensorFlow Version 2

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 image classification problems using Python and the TensorFlow library.

Version 2 of the TensorFlow templates contain updated structures and code like the previous image classification TensorFlow templates. I designed the templates to address binary class modeling exercises from beginning to end.

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

Tabular Data Analytics Project Templates Using Python and TensorFlow Version 9

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 TensorFlow library.

Version 9 of the TensorFlow templates contain updated structures and code like the previous TensorFlow 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.

Binary Classification Model for In-Vehicle Coupon Recommendation Using 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 In-Vehicle Coupon Recommendation dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: This dataset, available from UC Irvine’s Machine Learning Repository, studies whether a person will accept the coupon recommended to him under different driving scenarios.

ANALYSIS: The performance of the cross-validated TensorFlow models achieved an average accuracy benchmark of 0.7211 after running for 30 epochs. When we applied the final model to the test dataset, the model achieved an accuracy score of 0.7386.

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

Dataset Used: In-Vehicle Coupon Recommendation Data Set

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://archive-beta.ics.uci.edu/ml/datasets/in+vehicle+coupon+recommendation

The HTML formatted report can be found here on GitHub.