Binary-Class Tabular Model for Pistachio Identification Using Python and 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 Pistachio Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Pistachio nut has an important place in the agricultural economy; the efficiency of post-harvest industrial processes is crucial to maintaining its economic value. To provide this efficiency, the industry needs new methods and technologies for separating and classifying pistachios. In this study, the research team aimed to develop a classification model different from traditional separation methods based on image processing and artificial intelligence techniques.

A computer vision system (CVS) has been developed to distinguish two species of pistachios with different characteristics that address additional market types. The research team took 2148 sample images for these two kinds of pistachios with a high-resolution camera. They applied image processing, segmentation, and feature extraction techniques to the images of the pistachio samples.

After 100 trials, the best AutoKeras model processed the training dataset with an accuracy score of 95.63%. When we processed the test dataset with the final model, the model achieved an accuracy score of 93.25%.

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

Dataset Used: Pistachio Dataset

Dataset ML Model: Binary classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.23751/pn.v23i2.9686

The HTML formatted report can be found here on GitHub.

Multi-Class Model for Fetal Health Classification 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 Fetal Health Classification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset contains 2,126 records of features extracted from Cardiotocography exams. Cardiotocograms are a simple and cost accessible option to assess fetal health. The equipment works by sending ultrasound pulses and reading its response, thus highlighting fetal heart rate, fetal movements, uterine contractions, and more. Three expert obstetricians classified the outcomes into three classes: Normal, Suspect, and Pathological.

ANALYSIS: After a series of tuning trials, the best AutoKeras model processed the training dataset with an accuracy score of 93.30%. When we processed the test dataset with the final model, the model achieved an accuracy score of 88.08%.

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

Dataset Used: Fetal Health Classification Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.kaggle.com/andrewmvd/fetal-health-classification

One source of potential performance benchmarks: https://www.kaggle.com/andrewmvd/fetal-health-classification/code

The HTML formatted report can be found here on GitHub.

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.

Regression Model for Kaggle Tabular Playground Series 2021 August 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 August 2021 dataset is a regression modeling where we are trying to predict the value of a continuous variable.

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 7.9117. When we processed the test dataset with the final model, the model achieved a logarithmic loss of 7.9304.

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

Dataset Used: Kaggle Tabular Playground Series August 2021 Dataset

Dataset ML Model: Regression with numerical features

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

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