Binary-Class Tabular Model for Pumpkin Seeds Identification Using Python and XGBoost

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: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 88.40%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 88.68%.

CONCLUSION: In this iteration, the XGBoost model 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 Pumpkin Seeds Identification Using Python and TensorFlow Decision Forests

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: The Gradient Boosted Trees model performed the best with the training dataset. The model achieved an average accuracy benchmark of 88.56% using the 10-fold cross-validation method.

CONCLUSION: In this iteration, the Gradient Boosted Trees model 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 Pumpkin Seeds Identification Using Python and Scikit-Learn

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: The average performance of the machine learning algorithms achieved an accuracy benchmark of 87.06% after training. Furthermore, we selected Random Forest as the final model as it processed the training dataset with an accuracy score of 88.72% using the 10-fold cross-validation method.

CONCLUSION: In this iteration, the Random Forest model 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 XGBoost

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 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: The performance of the preliminary XGBoost model achieved a ROC_AUC benchmark of 0.5716. After a series of tuning trials, the final model processed the training dataset with a ROC_AUC score of 0.5761. When we processed the test dataset with the final model, the model achieved a ROC_AUC score of 0.5755.

CONCLUSION: In this iteration, the XGBoost model 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 Kaggle Tabular Playground 2022 August Using Python and TensorFlow Decision Forests

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: The performance of the preliminary Random Forest model achieved a ROC_AUC benchmark of 0.9703 on the training dataset. When we applied the finalized model to the test dataset, the model achieved a ROC_AUC score of 0.5473.

CONCLUSION: In this iteration, the Random Forest model 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 Kaggle Tabular Playground 2022 August Using Python and Scikit-Learn

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 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: The average performance of the machine learning algorithms achieved a ROC_AUC benchmark of 0.5449 using the training dataset. Furthermore, we selected Logistic Regression as the final model as it processed the training dataset with a final ROC_AUC score of 0.5853. When we processed the test dataset with the final model, the model achieved a ROC_AUC score of 0.5714.

CONCLUSION: In this iteration, the Logistic Regression model 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 XGBoost

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.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 91.49%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 92.78%. When we processed the test dataset with the final model, the model achieved an accuracy score of 91.16%.

CONCLUSION: In this iteration, the XGBoost model 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.

Binary-Class Tabular Model for Pistachio Identification Using Python and TensorFlow Decision Forests

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.

ANALYSIS: The performance of the preliminary Gradient Boosted Trees model achieved an accuracy benchmark of 99.30% on the training dataset. When we applied the finalized model to the test dataset, the model achieved an accuracy score of 90.93%.

CONCLUSION: In this iteration, the Gradient Boosted Trees model 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.