Breast cancer is the most common cancer in women worldwide, and accurate and early detection of breast cancer is vital in characterizing the disease. Transcriptomic expression is embedded abundant tumor and cell state information. However, selecting a good pipeline in applying mRNA expression is critical in downstream characteristics prediction. We designed a study that focused on determining the best combinations of preprocessing processes in predictions. We tested six normalization methods, two gene selection methods, and over ten machine learning algorithms. By using appropriate evaluation metrics, we recommend using FPKM normalization method combined with either gene selection method and employing RF for the purpose of breast cancer downstream prediction.