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This study explores the application of machine learning techniques for soil classification, utilizing a dataset of 805 soil samples from the Gayrettepe–Istanbul Airport metro line. It highlights the use of data preprocessing methods, including KNN imputation and SMOTE for class balancing, and demonstrates the effectiveness of advanced ML algorithms like XGBoost, LightGBM, and CatBoost, achieving classification accuracy rates exceeding 90%. The research emphasizes the potential of machine learning to streaml

Author
st125745
Language
EN