This chapter describes the detailed implementation and evaluation of a supervised machine learning pipeline to predict the transfection efficiency (TE) of lipid nanoparticles. A comprehensive workflow utilizing a curated dataset of over 600 LNP formulations, each annotated with binary TE outcomes, is presented. Key preprocessing steps, including the calculation of molecular descriptors (e.g., molecular weight, lipophilicity, and polar surface area), handling missing data, feature selection, and normalization, are clearly outlined. The chapter details the construction and validation of a Random Forest Classifier (RFC) using five-fold cross-validation and presents initial results highlighting the accuracy and feature importance for TE predictions. Additionally, critical limitations related to data imbalance and model generalizability are discussed, setting the stage for exploring semi-supervised learning enhancements in subsequent chapters.