DNA Sequence Classification using Machine Learning

This work presents a novel approach to enhancing the accuracy and efficacy of DNA sequence categorization through the use of Machine Learning algorithms. The proposed DNA Sequencing Classifier leverages state-of-the-art Machine Learning algorithms to classify DNA sequences into predefined categories, such as species identification. DNA sequencing has revolutionized genetics across medicine, evolution, and more. However, accurate categorization of DNA sequences remains challenging. Machine Learning offers a solution by automating this process, improving precision, and revealing hidden patterns. This project aims to create an efficient DNA Sequence Classifier using advanced Machine Learning techniques. By automating classification, we enhance accuracy and speed up genetic research. Starting with data preprocessing, raw DNA sequences are refined for reliability. Extracted features serve as inputs for Machine Learning models, with the best classifier chosen based on performance. The DNA Sequencing Classifier is rigorously evaluated, compared with existing methods through k-mer counting. The fusion of DNA sequencing and Machine Learning promises streamlined DNA classification, accelerating research and deepening genetic understanding.