Creating accurate predictions in the stock market has always been a great challenge in finance. With the rise of machine learning as the next level in the forecasting area, this research compares four machine learning models and their accuracy for forecasting three well-known stocks traded in the NYSE in the short term over the period from March 2020 to May 2022. We deploy, develop, and hypertune XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models and report the models that produce the highest accuracies from our evaluation metrics: RMSE, MAPE, and MPE. Co-sponsored by: Langara College Speaker(s): Steven Whang , Niha Siddikha Sachin Room: HS301, 1000 KLO Rd., Kelowna, Kelowna, British Columbia, Canada, V1Y 4X8, Virtual: https://events.vtools.ieee.org/m/337514