Tag: deep learning

Multi-Class Image Classification Model for Grapevine Leaves Image Using TensorFlow Take 1

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 Grapevine Leaves Image Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. Therefore, the species of grapevine leaves are essential factors in price and taste. The research team deployed deep learning-based classification models in this study to identify grapevine leaves. For this study, 500 images of vine leaves make up the dataset.

ANALYSIS: The Xception model’s performance achieved an accuracy score of 89.78% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 86.00%.

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

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Grapevine Leaves Image Dataset

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.measurement.2021.110425

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Model for Date Fruits Classification Using Python and AutoKeras

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 Date Fruits Dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Many factors determine the type of fruit with external appearance features such as color, length, diameter, and shape. However, classifying the variety of fruits simply by looking at their outward appearance requires expertise and a great effort and is also time-consuming. This study aims to classify the types of date fruit using machine-learning methods. The research team obtained 898 images of seven different date fruit types for this project via a computer vision system. The research team was hopeful that machine learning methods could successfully classify date fruit types.

ANALYSIS: After 70 trials, the best AutoKeras model processed the training dataset with an accuracy score of 91.87%.

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

Dataset Used: Date Fruits Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1155/2021/4793293

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Model for Date Fruits Classification Using Python and TensorFlow

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 Date Fruits Dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Many factors determine the type of fruit with external appearance features such as color, length, diameter, and shape. However, classifying the variety of fruits simply by looking at their outward appearance requires expertise and a great effort and is also time-consuming. This study aims to classify the types of date fruit using machine-learning methods. The research team obtained 898 images of seven different date fruit types for this project via a computer vision system. The research team was hopeful that machine learning methods could successfully classify date fruit types.

ANALYSIS: The average performance of the cross-validated TensorFlow models achieved an accuracy benchmark of 92.87%.

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

Dataset Used: Date Fruits Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1155/2021/4793293

The HTML formatted report can be found here on GitHub.

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

INTRODUCTION: In 2912, the Spaceship Titanic, an interstellar passenger liner, launched with almost 13,000 passengers on board. The vessel set out on its maiden voyage transporting emigrants from our solar system to three newly habitable exoplanets orbiting nearby stars.

While rounding Alpha Centauri en route to its first destination, the unwary Spaceship Titanic collided with a spacetime anomaly hidden within a dust cloud. Sadly, it met a similar fate as its namesake from 1000 years before. Though the ship stayed intact, almost half of the passengers were transported to an alternate dimension!

This incident presented a challenge to the data scientists of the future. To help rescue crews and retrieve the lost passengers, the data scientists tried to build a model that predicted which passengers were transported by the anomaly using records recovered from the spaceship’s damaged computer system.

ANALYSIS: After 30 trials, the best AutoKeras model processed the training dataset with the best accuracy score of 79.96%. When we processed the test dataset with the final model, the model achieved an accuracy score of 50.68%.

CONCLUSION: In this iteration, the AutoKeras model did not appear to be a suitable algorithm for modeling this dataset.

Dataset Used: Spaceship Titanic Dataset

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

Dataset Reference: https://www.kaggle.com/competitions/spaceship-titanic

One source of potential performance benchmarks: https://www.kaggle.com/competitions/spaceship-titanic/leaderboard

The HTML formatted report can be found here on GitHub.

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

INTRODUCTION: In 2912, the Spaceship Titanic, an interstellar passenger liner, launched with almost 13,000 passengers on board. The vessel set out on its maiden voyage transporting emigrants from our solar system to three newly habitable exoplanets orbiting nearby stars.

While rounding Alpha Centauri en route to its first destination, the unwary Spaceship Titanic collided with a spacetime anomaly hidden within a dust cloud. Sadly, it met a similar fate as its namesake from 1000 years before. Though the ship stayed intact, almost half of the passengers were transported to an alternate dimension!

This incident presented a challenge to the data scientists of the future. To help rescue crews and retrieve the lost passengers, the data scientists tried to build a model that predicted which passengers were transported by the anomaly using records recovered from the spaceship’s damaged computer system.

ANALYSIS: The average performance of the cross-validated TensorFlow models achieved an accuracy benchmark of 74.75% after training. When we processed the test dataset with the final model, the model achieved an accuracy score of 74.23%.

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

Dataset Used: Spaceship Titanic Dataset

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

Dataset Reference: https://www.kaggle.com/competitions/spaceship-titanic

One source of potential performance benchmarks: https://www.kaggle.com/competitions/spaceship-titanic/leaderboard

The HTML formatted report can be found here on GitHub.