This data set is provided by the UCI Machine Learning Repository at the following web address. https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset

The data set summary is quoted below. Let’s have a quick read. . .

"A computer vision system was developed to distinguish seven different registered varieties of dry beans with similar features in order to obtain uniform seed classification. For the classification model, images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. Bean images obtained by the computer vision system were subjected to segmentation and feature extraction stages, and a total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains.”

This goal of this project is to use the knowledge I have gained during HarvardX’s Data Science Professional Certificate program to compose several multi-class classification algorithms in an attempt to match or beat the accuracy reported by Koklu and Oskan in their paper titled “Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques”. They used a support vector machine to achieve a 93.13% accuracy rate. You can read the abstract of their paper online at ScienceDirect.com by following this link.

https://www.sciencedirect.com/science/article/abs/pii/S0168169919311573?via%3Dihub

I have only read the abstract and not the paper itself in order to preserve my own creative responses in solving this multi-class classification problem. I hope you enjoy reading this project