Dry Bean ve Algerian Forest Fires veri setleri üzerinde SVM ve XGBoost ile sınıflandırma ve regresyon analizi içeren kapsamlı makine öğrenmesi projesi.

Automated classification of 7 different types of dry beans using machine learning techniques. This project leverages computer vision-extracted geometric and shape features (such as Area, Perimeter, and Shape Factors) to accurately identify bean varieties including Barbunya, Bombay, Cali, Dermason, Horoz, Seker, and Sira.

This project analyzed and compared the performance of 16 machine learning models on a supervised classification task using the Dry Bean dataset. This project pursued two objectives: (1) Measure how accurately each model classifies unseen bean samples, and (2) Determine each model’s runtime for its classification training and testing process.