Binary-Class Tabular Classification Model for Raisin Grains 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 Raisin Grains Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to classify two different varieties of raisin grown in Turkey. The dataset contains the measurements for 900 raisin grain images. The image further broke down into seven major morphological features for each grain of raisin.

ANALYSIS: The performance of the preliminary TensorFlow model achieved an accuracy benchmark of 86.05%. When we processed the test dataset with the final model, the model achieved an accuracy score of 91.11%.

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

Dataset Used: Raisin 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/10.30855/gmbd.2020.03.03

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Classification Model for Raisin Grains 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 Raisin Grains Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to classify two different varieties of raisin grown in Turkey. The dataset contains the measurements for 900 raisin grain images. The image further broke down into seven major morphological features for each grain of raisin.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 85.92%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 86.17%. When we processed the test dataset with the final model, the model achieved an accuracy score of 86.66%.

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

Dataset Used: Raisin 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/10.30855/gmbd.2020.03.03

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Classification Model for Raisin Grains 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 Raisin Grains Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to classify two different varieties of raisin grown in Turkey. The dataset contains the measurements for 900 raisin grain images. The image further broke down into seven major morphological features for each grain of raisin.

ANALYSIS: The performance of the preliminary Random Forest model achieved an accuracy benchmark of 96.05% on the training dataset. When we applied the finalized model to the test dataset, the model achieved an accuracy score of 86.67%.

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

Dataset Used: Raisin 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/10.30855/gmbd.2020.03.03

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Classification Model for Raisin Grains 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 Raisin Grains Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to classify two different varieties of raisin grown in Turkey. The dataset contains the measurements for 900 raisin grain images. The image further broke down into seven major morphological features for each grain of raisin.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 84.11% using the training dataset. Furthermore, we selected Logistic Regression as the final model as it processed the training dataset with a final accuracy score of 86.29%. When we processed the test dataset with the final model, the model achieved an accuracy score of 91.11%.

CONCLUSION: In this iteration, the Logistic Regression model appeared to be suitable for modeling this dataset.

Dataset Used: Raisin 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/10.30855/gmbd.2020.03.03

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Classification Model for Rice Cammeo Osmancik 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 Rice Cammeo Osmancik Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Rice is one of the most widely produced and consumed cereal crops globally. The crop is also the main sustenance for many countries because of its economic and nutritious nature. However, before rice reaches the consumers, it must go through many manufacturing steps such as cleaning, color sorting, and classification. In this study, the research team developed a computerized vision system to classify two proprietary rice species. The dataset contains the measurements for 3,810 rice grain images. The grain image broke down into seven major morphological features for each grain of rice.

ANALYSIS: The performance of the preliminary TensorFlow model achieved an accuracy benchmark of 92.50%. When we processed the test dataset with the final model, the model achieved an accuracy score of 92.91%.

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

Dataset Used: Rice Dataset Cammeo and Osmancik

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.18201/ijisae.2019355381

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Classification Model for Rice Cammeo Osmancik 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 Rice Cammeo Osmancik Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Rice is one of the most widely produced and consumed cereal crops globally. The crop is also the main sustenance for many countries because of its economic and nutritious nature. However, before rice reaches the consumers, it must go through many manufacturing steps such as cleaning, color sorting, and classification. In this study, the research team developed a computerized vision system to classify two proprietary rice species. The dataset contains the measurements for 3,810 rice grain images. The grain image broke down into seven major morphological features for each grain of rice.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 92.79%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 92.97%. When we processed the test dataset with the final model, the model achieved an accuracy score of 92.65%.

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

Dataset Used: Rice Dataset Cammeo and Osmancik

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.18201/ijisae.2019355381

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Classification Model for Rice Cammeo Osmancik 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 Rice Cammeo Osmancik Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Rice is one of the most widely produced and consumed cereal crops globally. The crop is also the main sustenance for many countries because of its economic and nutritious nature. However, before rice reaches the consumers, it must go through many manufacturing steps such as cleaning, color sorting, and classification. In this study, the research team developed a computerized vision system to classify two proprietary rice species. The dataset contains the measurements for 3,810 rice grain images. The grain image broke down into seven major morphological features for each grain of rice.

ANALYSIS: The performance of the preliminary Random Forest model achieved an accuracy benchmark of 96.73% on the training dataset. When we applied the finalized model to the test dataset, the model achieved an accuracy score of 92.65%.

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

Dataset Used: Rice Dataset Cammeo and Osmancik

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.18201/ijisae.2019355381

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Classification Model for Rice Cammeo Osmancik 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 Rice Cammeo Osmancik Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Rice is one of the most widely produced and consumed cereal crops globally. The crop is also the main sustenance for many countries because of its economic and nutritious nature. However, before rice reaches the consumers, it must go through many manufacturing steps such as cleaning, color sorting, and classification. In this study, the research team developed a computerized vision system to classify two proprietary rice species. The dataset contains the measurements for 3,810 rice grain images. The grain image broke down into seven major morphological features for each grain of rice.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 91.78% using the training dataset. Furthermore, we selected Logistic Regression as the final model as it processed the training dataset with a final accuracy score of 92.97%. When we processed the test dataset with the final model, the model achieved an accuracy score of 92.91%.

CONCLUSION: In this iteration, the Logistic Regression model appeared to be suitable for modeling this dataset.

Dataset Used: Rice Dataset Cammeo and Osmancik

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.18201/ijisae.2019355381

The HTML formatted report can be found here on GitHub.