The MRAI data analysis pipeline provides a structured, R-based workflow for rigorously handling geochemical and spatial data in mineral exploration and geospatial assessments. It emphasizes data integrity, reproducibility, and comprehensive validation, ensuring that both raw and processed datasets are ready for downstream modeling and decision-making.

The MRAI data transformation pipeline focuses on advanced transformations, correlation analysis, and factor extraction within geochemical datasets. By incorporating transformations, systematic imputation, and factor analysis, raw or partially processed data are refined to support robust modeling, feature engineering, and deeper geochemical insights.

The MRAI machine learning pipeline extends prior data transformations by applying both supervised and unsupervised learning techniques to geochemical datasets. This focuses on dimensionality reduction for visualization, cluster identification, and initial modeling strategies to derive geochemical insights.