Developed a machine learning classifier for Life Edit Therapeutics to differentiate between edited and unedited cells based on RNA gene expression data. Trained and benchmarked multiple model architectures, and conducted PCA for dimensionality reduction. For interpretability, genes were clustered with K-Means based on their functional descriptions, and each cluster was assigned a natural-language functional label so the model's outputs could be tied back to biological meaning, supporting efforts to treat complex genetic disorders.