Mosquito-Genus-Classifier-

Advances in machine learning have enabled accurate prediction and classification of biological data (Schrider & Kern, 2018). Supervised learning trains models on known data to classify new samples, which has applications in population genetics, evolution, and disease tracking (Xu & Jackson, 2019). In this project, supervised learning will be used to classify two mosquito genera, Aedes and Anopheles, which are vectors for diseases such as Zika virus and malaria (WHO, 2022; WHO, 2023). Controlling these vectors is crucial to managing disease spread (von Seidlein et al., 2017). It is aimed to build a random forest classifier using differences in Cytochrome c oxidase subunit I (COI) mitochondrial gene sequences to distinguish the two genera (da Silva et al., 2020). The code for this anayslis and final report is avilable!