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 average performance of the machine learning algorithms achieved an accuracy benchmark of 98.65% using the training dataset. Furthermore, we selected k-Nearest Neighbors as the final model as it processed the training dataset with a final accuracy score of 99.56%. When we processed the test dataset with the final model, the model achieved an accuracy score of 99.66%.
CONCLUSION: In this iteration, the k-Nearest Neighbors 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.