Assignment2_Comparing-Classifiers

This project evaluates the performance of Random Forest and Logistic Regression models for gene sequence classification. Visualizations were developed to illustrate sequence length distributions, k-mer frequency proportions, and feature importance, providing insights into model performance and key classification features.

Attribution:

Primary Author:

Mirza Ahmadi

Secondary Contributor:

Phoenix Armstrong, who enhanced the script by implementing a data filtration function, implementing a plotting function for lollipop plots, and increasing code efficiency across the RMarkdown script.

Additional Contributors:

Dilma Karunathilake and Fatemeh Asgarian, who provided valuable feedback and ideas.