Binary-Class Tabular Model for Liver Disease Patients 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 Liver Disease Patients dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: This dataset contains over 30,000 cases of liver disease diagnosis results. The researcher trained machine learning models using this dataset to test the feasibility of applying machine learning techniques for making diagnostic predictions.

ANALYSIS: After 50 trials, the best AutoKeras model processed the training dataset with the best accuracy score of 99.78%.

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

Dataset Used: Liver Disease Patients Dataset

Dataset ML Model: Binary-Class classification with numerical and categorical features

Dataset Reference: https://www.kaggle.com/datasets/abhi8923shriv/liver-disease-patient-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/abhi8923shriv/liver-disease-patient-dataset/code

The HTML formatted report can be found here on GitHub.

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

INTRODUCTION: This dataset comes from research by Semeion, Research Center of Sciences of Communication. The original aim of the research was to correctly classify the type of surface defects in stainless steel plates, with six types of possible defects (plus “other”). The Input vector was made up of 27 indicators that approximately the geometric shape of the defect and its outline. According to the research paper, Semeion was commissioned by the Centro Sviluppo Materiali (Italy) for this task, and therefore it is not possible to provide details on the nature of the 27 indicators used as Input vectors or the types of the six classes of defects.

ANALYSIS: After 100 trials, the best AutoKeras model processed the training dataset with an accuracy score of 82.30%.

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

Dataset Used: Steel Plates Faults

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://archive-beta.ics.uci.edu/ml/datasets/steel+plates+faults

One source of potential performance benchmarks: https://www.kaggle.com/uciml/faulty-steel-plates

The HTML formatted report can be found here on GitHub.

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

INTRODUCTION: Pumpkin seeds are frequently consumed as confection worldwide because of their adequate amount of protein, fat, carbohydrate, and mineral contents. The research team carried out a study on the two most important types of pumpkin seed, “Ürgüp Sivrisi” and “Çerçevelik,” generally grown in Ürgüp and Karacaören regions in Turkey. Furthermore, the morphological measurements of 2500 pumpkin seeds of both varieties were captured using the gray and binary forms of threshold techniques.

ANALYSIS: After 100 trials, the best AutoKeras model processed the training dataset with an accuracy score of 87.88%.

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

Dataset Used: Pumpkin Seeds 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/10.1007/s10722-021-01226-0

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Model for Kaggle Tabular Playground 2022 August 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 Kaggle Tabular Playground 2022 August dataset is a binary-class modeling situation where we attempt to predict one of 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. This data represents the results of an extensive product testing study. For each product code, the research team provided several product attributes and several measurement values for each product, representing various lab testing methods.

Each product is used in a simulated real-world environment experiment and absorbs a certain amount of fluid to see whether it fails. The project task is to use the data to predict individual product failures of new codes with their lab test results.

ANALYSIS: After 100 trials, the best AutoKeras model processed the training dataset with a ROC_AUC score of 0.4824. When we processed the test dataset with the final model, the model achieved a ROC_AUC score of 0.5573.

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

Dataset Used: Kaggle Tabular Playground 2022 August

Dataset ML Model: Binary classification with numerical features

Dataset Reference: https://www.kaggle.com/competitions/tabular-playground-series-aug-2022

One source of potential performance benchmarks: https://www.kaggle.com/competitions/tabular-playground-series-aug-2022/leaderboard

The HTML formatted report can be found here on GitHub.

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.