Project focused on classifying seven types of dry beans using machine learning techniques. The dataset includes 16 key characteristics such as area, perimeter, and shape factors to predict the bean type. Objective: Develop a robust classification model using algorithms like SVM, KNN, and Random Forest for accurate classification and analysis.
Dry Bean Classification using Machine Learning for Multiclass Prediction and Agricultural Data Analysis.
Java implementation of a K-Nearest Neighbours classifier with four weighted-polling methods, including Naive Bayes-style class-frequency weighting, for Dry Bean multiclass classification, with leave-one-out testing and feature-subset search. Code for Morsidi (2026), ASM Science Journal.
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