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 Pizza vs. Ice Cream dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains over 1,000 images of pizza and ice cream. The researcher collected these images to investigate the machine learning model’s ability to understand and distinguish the basic features of pizza and ice cream.

ANALYSIS: The EfficientNetV2L model’s performance achieved an accuracy score of 96.15% after five epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 96.22%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2L CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Pizza vs. Ice Cream

Dataset Reference: https://www.kaggle.com/datasets/hemendrasr/pizza-vs-ice-cream

One source of potential performance benchmarks: https://www.kaggle.com/datasets/hemendrasr/pizza-vs-ice-cream/code

The HTML formatted report can be found here on GitHub.

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 Pizza vs. Ice Cream dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains over 1,000 images of pizza and ice cream. The researcher collected these images to investigate the machine learning model’s ability to understand and distinguish the basic features of pizza and ice cream.

ANALYSIS: The VGG19 model’s performance achieved an accuracy score of 92.79% after ten epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 97.16%.

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

Dataset ML Model: Binary classification with numerical features

Dataset Used: Pizza vs. Ice Cream

Dataset Reference: https://www.kaggle.com/datasets/hemendrasr/pizza-vs-ice-cream

One source of potential performance benchmarks: https://www.kaggle.com/datasets/hemendrasr/pizza-vs-ice-cream/code

The HTML formatted report can be found here on GitHub.

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 Pizza vs. Ice Cream dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains over 1,000 images of pizza and ice cream. The researcher collected these images to investigate the machine learning model’s ability to understand and distinguish the basic features of pizza and ice cream.

ANALYSIS: The MobileNetV2 model’s performance achieved an accuracy score of 96.15% after five epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 96.22%.

CONCLUSION: In this iteration, the TensorFlow MobileNetV2 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Pizza vs. Ice Cream

Dataset Reference: https://www.kaggle.com/datasets/hemendrasr/pizza-vs-ice-cream

One source of potential performance benchmarks: https://www.kaggle.com/datasets/hemendrasr/pizza-vs-ice-cream/code

The HTML formatted report can be found here on GitHub.

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 Pizza vs. Ice Cream dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains over 1,000 images of pizza and ice cream. The researcher collected these images to investigate the machine learning model’s ability to understand and distinguish the basic features of pizza and ice cream.

ANALYSIS: The ResNet152V2 model’s performance achieved an accuracy score of 98.08% after five epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 96.22%.

CONCLUSION: In this iteration, the TensorFlow ResNet152V2 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Pizza vs. Ice Cream

Dataset Reference: https://www.kaggle.com/datasets/hemendrasr/pizza-vs-ice-cream

One source of potential performance benchmarks: https://www.kaggle.com/datasets/hemendrasr/pizza-vs-ice-cream/code

The HTML formatted report can be found here on GitHub.

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 Pizza vs. Ice Cream dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains over 1,000 images of pizza and ice cream. The researcher collected these images to investigate the machine learning model’s ability to understand and distinguish the basic features of pizza and ice cream.

ANALYSIS: The Xception model’s performance achieved an accuracy score of 99.04% after five epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 98.11%.

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

Dataset ML Model: Binary classification with numerical features

Dataset Used: Pizza vs. Ice Cream

Dataset Reference: https://www.kaggle.com/datasets/hemendrasr/pizza-vs-ice-cream

One source of potential performance benchmarks: https://www.kaggle.com/datasets/hemendrasr/pizza-vs-ice-cream/code

The HTML formatted report can be found here on GitHub.