Advanced Machine Learning Approaches for Accurate Rock Type Classification and Comprehensive Analysis in Mining Using Geochemical Data

Accurate rock type categorization is critical for all stages of mining operations, such as exploration, extraction, and processing. Traditionally, these classifications were based mainly on geological expertise and empirical procedures, which, while successful, are sometimes time-consuming and prone to human error. The mining sector is increasingly relying on advanced machine learning (ML) techniques to increase rock classification and resource extraction efficiency. A sophisticated Adaptive Boosted Neural Network (ML) framework is developed for accurate rock type classification and extensive analysis in mining with geochemical data. The dataset is preprocessed to remove unnecessary data, and prominent features are selected using feature engineering approaches. Dataset is divided into train and test sets to allow for rigorous model training and validation. The AdaBoost Neural Network is at the heart of this system, combining the advantages of boosting techniques and neural networks to obtain improved classification results. A complete model selection procedure uses probabilistic and resampling techniques to determine the best model configuration. Finally, this project is implemented in Python. Efficiency analysis is performed using a variety of criteria, including $97 \%$ accuracy, $98 \%$ precision, and $97 \%$ recall and F1-score.