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 Kaggle Tabular Playground 2022 August 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 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 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: The performance of the preliminary XGBoost model achieved a ROC_AUC benchmark of 0.5716. After a series of tuning trials, the final model processed the training dataset with a ROC_AUC score of 0.5761. When we processed the test dataset with the final model, the model achieved a ROC_AUC score of 0.5755.

CONCLUSION: In this iteration, the XGBoost model 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.

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

INTRODUCTION: Pistachio nut has an important place in the agricultural economy; the efficiency of post-harvest industrial processes is crucial to maintaining its economic value. To provide this efficiency, the industry needs new methods and technologies for separating and classifying pistachios. In this study, the research team aimed to develop a classification model different from traditional separation methods based on image processing and artificial intelligence techniques.

A computer vision system (CVS) has been developed to distinguish two species of pistachios with different characteristics that address additional market types. The research team took 2148 sample images for these two kinds of pistachios with a high-resolution camera. They applied image processing, segmentation, and feature extraction techniques to the images of the pistachio samples.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 91.49%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 92.78%. When we processed the test dataset with the final model, the model achieved an accuracy score of 91.16%.

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

Dataset Used: Pistachio 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/10.23751/pn.v23i2.9686

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Classification Model for Dry Bean 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 Dry Bean Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: There is a wide range of genetic diversity in dry beans, the most produced one among the edible legume crops in the world. Seed classification is essential for the marketing and production of sustainable agricultural systems. The primary objective of this research study is to provide a method for obtaining uniform seed varieties from crop production. The research team developed a computer vision system to classify seven different types of dry beans with similar features. For the classification model, the researchers used a high-resolution camera to gather 13,611 images of dry beans.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 92.61%. 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 91.77%.

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

Dataset Used: Dry Bean 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/10.1016/j.compag.2020.105507

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Classification Model for Avila Bible 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 Avila Bible Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The Avila dataset includes 800 images extracted from the “Avila Bible,” a giant Latin copy of the whole Bible produced during the XII century between Italy and Spain. The paleographic analysis of the manuscript has identified the presence of 12 transcribers; however, each transcriber did not transcribe the same number of pages. The prediction task is to associate each pattern to one of the 12 transcribers labeled as A, B, C, D, E, F, G, H, I, W, X, and Y. The research team normalized the data using the Z-normalization method and divided the dataset into two portions, training and test. The training set contains 10,430 samples, while the test set contains 10,437 samples.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 86.67%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 99.79%. When we processed the test dataset with the final model, the model achieved an accuracy score of 99.81%.

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

Dataset Used: Avila Bible Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://archive-beta.ics.uci.edu/ml/datasets/avila

One source of potential performance benchmarks: https://www.sciencedirect.com/science/article/abs/pii/S0952197618300721

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 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.

Multi-Class Tabular Classification Model for Durum Wheat 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 Durum Wheat Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Wheat is the main ingredient of most common food products in many people’s daily lives. Obtaining good quality wheat kernels is an essential matter for food supplies. In this study, the research team attempted to examine and classify type-1252 durum wheat kernels to obtain top-quality crops based on their vitreousness. The researchers used a total of 236 morphological, color, wavelet, and gaborlet features to classify durum wheat kernels and foreign objects by training several Artificial Neural Networks (ANNs) with different amounts of elements based on the feature rank list obtained with the ANOVA test.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 99.30%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 99.60%. When we processed the test dataset with the final model, the model achieved an accuracy score of 99.55%.

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

Dataset Used: Durum Wheat 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/10.1016/j.compag.2019.105016

The HTML formatted report can be found here on GitHub.