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Dry Beans Classification Using Machine Learning

A dataset containing over 13k samples of dry beans geometric features is being analysed using machine learning (ML) and deep learning (DL) techniques with the goal to automatically classify the bean species. First the original dataset was reduced to eliminate redundant features (too strongly correlated and echoing others). Then the dataset was visualised and analysed with machine learning techniques: Multinomial Bayes, Support Vector Machines, Decision Tree, Random Forest, Voting Classifier and Artificial Neural Network. The overall accuracies obtained were in range: 88.35 – 93.61%.