This project presents an interdisciplinary approach that combines genomics and computing to identify human relatedness by predicting an individuals ethnicity and geographic region using mitochondrial DNA (mtDNA) Hypervariable region 1 and 2. Unlike nuclear DNA, mtDNA is maternally inherited and takes a long period of time to degrade. The ethnicity model is a Gradient Boosting model with accuracy 95% and the geographic location is a Random Forest model with accuracy 90%. The data obtained from GenBank to train the model has also been validated to prove that there is variation between the samples using Analysis of Molecular Variance (AMOVA) and a web application has been developed using React and Flask api to integrate the machine learning model.
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