Binary Class Image Classification Deep Learning Model for CycleGAN Cezanne vs. Photo Using TensorFlow Take 3

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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the DenseNet121 architecture to make predictions.

ANALYSIS: In this iteration, the DenseNet121 model’s performance achieved an accuracy score of 99.80% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 98.90%. Finally, the final model processed the test dataset with an accuracy score of 99.87%.

CONCLUSION: In this iteration, the DenseNet121-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Cezanne vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for CycleGAN Cezanne vs. Photo 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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the ResNet50V2 architecture to make predictions.

ANALYSIS: In this iteration, the ResNet50V2 model’s performance achieved an accuracy score of 99.49% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 98.53%. Finally, the final model processed the test dataset with an accuracy score of 98.51%.

CONCLUSION: In this iteration, the ResNet50V2-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Cezanne vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for CycleGAN Cezanne vs. Photo 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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the InceptionV3 architecture to make predictions.

ANALYSIS: In this iteration, the InceptionV3 model’s performance achieved an accuracy score of 99.65% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 98.24%. Finally, the final model processed the test dataset with an accuracy score of 99.75%.

CONCLUSION: In this iteration, the InceptionV3-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Cezanne vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for CycleGAN Monet vs. Photo Using TensorFlow Take 4

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 CycleGAN Monet vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the VGG16 architecture to make predictions.

ANALYSIS: In this iteration, the VGG16 model’s performance achieved an accuracy score of 96.81% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 94.49%. Finally, the final model processed the test dataset with an accuracy score of 96.56%.

CONCLUSION: In this iteration, the VGG16-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Monet vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for CycleGAN Monet vs. Photo Using TensorFlow Take 3

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 CycleGAN Monet vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the DenseNet121 architecture to make predictions.

ANALYSIS: In this iteration, the DenseNet121 model’s performance achieved an accuracy score of 99.30% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 96.53%. Finally, the final model processed the test dataset with an accuracy score of 95.64%.

CONCLUSION: In this iteration, the DenseNet121-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Monet vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for CycleGAN Monet vs. Photo 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 CycleGAN Monet vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the ResNet50V2 architecture to make predictions.

ANALYSIS: In this iteration, the ResNet50V2 model’s performance achieved an accuracy score of 99.08% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 97.96%. Finally, the final model processed the test dataset with an accuracy score of 95.87%.

CONCLUSION: In this iteration, the ResNet50V2-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Monet vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Class Image Classification Deep Learning Model for CycleGAN Monet vs. Photo 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 CycleGAN Monet vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the InceptionV3 architecture to make predictions.

ANALYSIS: In this iteration, the InceptionV3 model’s performance achieved an accuracy score of 99.54% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 97.89%. Finally, the final model processed the test dataset with an accuracy score of 98.62%.

CONCLUSION: In this iteration, the InceptionV3-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Monet vs. Photo Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.

Binary Classification Model for Bondora P2P Lending Using Python and 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 Bondora P2P Lending dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The Kaggle dataset owner retrieved this dataset from Bondora, a leading European peer-to-peer lending platform. The data comprises demographic and financial information of the borrowers with defaulted and non-defaulted loans between February 2009 and July 2021. For investors, “peer-to-peer lending” or “P2P” offers an attractive way to diversify portfolios and enhance long-term performance. However, to make effective decisions, investors want to minimize the risk of default of each lending decision and realize the return that compensates for the risk. Therefore, we will predict the default risk by focusing on the “DefaultDate” attribute as the target.

ANALYSIS: The performance of the cross-validated TensorFlow models achieved an average accuracy benchmark of 0.9726 after running for 20 epochs. When we applied the final model to Kaggle’s test dataset, the model achieved an accuracy score of 0.9075.

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

Dataset Used: Kaggle Bondora P2P Lending Loan Data

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://www.kaggle.com/sid321axn/bondora-peer-to-peer-lending-loan-data

Dataset Attribute Description: https://www.bondora.com/en/public-reports

One potential source of performance benchmark: https://www.kaggle.com/sid321axn/bondora-peer-to-peer-lending-loan-data/code

The HTML formatted report can be found here on GitHub.