Application of Classification Machine Learning Models for Grouping Laboratory Samples into Kazakh Clans

This research bridges traditional Kazakh heritage with modern science by developing a machine learning model to classify Kazakh clans using Y-chromosome data. This paper introduces an innovative approach for assigning Y-chromosomes to specific clans. Methodology leverages a dataset that includes 3,099 laboratory samples with clan affiliation and corresponding Short Tandem Repeat profiles. Utilizing a dataset, I employed several well-known machine learning algorithms (Random Forest, Decision tree, Support vector machine, Bayesian model) to predict clan affiliations, highlighting the potential of data analytics in enriching our understanding of cultural identities. The evaluation of the model's effectiveness involved rigorous testing against a diverse range of samples, ensuring a scalable and adaptable tool for future genomic research.