This dataset and code supports the study titled: "Machine Learning-based Investigation of the Relationship Between Plasma Emission Spectra and Biological Responses." This research focuses on developing and validating machine learning models to predict biological outcomes (24-hour and 48-hour MTT cell viability assays) from the optical emission spectra and operational parameters of two custom-built helium plasma jets. The uploaded file, Plasma Prj.rar, contains all the necessary components to reproduce the results of this study. The contents are organized as follows: 1. Codes This folder contains all Python scripts used for the entire workflow, including: Data preprocessing and normalization. Training, validation, and testing of all machine learning models (e.g., Random Forest, CNN, etc.). Final analysis and figure generation. 2. DataSets This folder contains the datasets from both plasma jets, divided into raw and processed versions. A. Raw Data: This subfolder contains the original, unprocessed data files for both plasma devices. B. Final DataSets (Processed and Normalized): This subfolder contains the final, analysis-ready CSV files: train_processed.csv: The training partition (n=520) from the primary plasma jet (Jet 1). test_processed.csv: The hold-out test partition (n=130) from the primary plasma jet (Jet 1). processed_second_jet.csv: The complete, processed dataset for the external validation device (Jet 2, n=62).