Multi-Class Image Classification Model for Reptiles and Amphibians Image Using TensorFlow Take 2

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 a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Reptiles and Amphibians Image dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Reptiles and amphibians are crucial components of the ecosystems in which they reside. They are also useful indicators of the condition of the environment. The presence of a variety of amphibians and reptiles in an area suggests that it is stable and capable of supporting plant and animal life.

The dataset contains ten different classes of reptiles and amphibians images. The research team have resized the images to either (300px, n) or (n,300px) where n is a pixel size less than 300px.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 87.80% after ten epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 83.83%.

CONCLUSION: In this iteration, the TensorFlow InceptionV3 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Reptiles and Amphibians Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/vencerlanz09/reptiles-and-amphibians-image-dataset

One source of potential performance benchmarks: https://www.kaggle.com/code/vencerlanz09/reptiles-and-amphibians-classification-using-cnn/notebook

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Reptiles and Amphibians Image Using TensorFlow Take 1

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 a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Reptiles and Amphibians Image dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Reptiles and amphibians are crucial components of the ecosystems in which they reside. They are also useful indicators of the condition of the environment. The presence of a variety of amphibians and reptiles in an area suggests that it is stable and capable of supporting plant and animal life.

The dataset contains ten different classes of reptiles and amphibians images. The research team have resized the images to either (300px, n) or (n,300px) where n is a pixel size less than 300px.

ANALYSIS: The VGG19 model’s performance achieved an accuracy score of 87.09% after ten epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 76.00%.

CONCLUSION: In this iteration, the TensorFlow VGG19 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Reptiles and Amphibians Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/vencerlanz09/reptiles-and-amphibians-image-dataset

One source of potential performance benchmarks: https://www.kaggle.com/code/vencerlanz09/reptiles-and-amphibians-classification-using-cnn/notebook

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 Pumpkin Seeds Identification Using Python and TensorFlow

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 cross-validated TensorFlow models achieved an accuracy benchmark of 87.40%.

CONCLUSION: In this iteration, TensorFlow 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 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 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.