Shuo Zhang graduated from Columbia University with a PhD degree in Chemical Engineering. The focus of her academic research was the designing of a protocol to synthesize layer-by-layer polymer films on nano-surfaces, investigate the dynamics and kinetics of this system, and construct quantitative models to predict the thickness and surface topography. After graduation, she worked as a senior process engineer for two years at GlobalFoundries, a top manufacturer of semi-conductors. There Shuo worked on multiple projects involving the improvement of the design of module unit processes for 20nm and 14nm chips. She utilized her skills to analyze data from the production line, build models to screen and optimize unit production, improve product design, and enhance production proficiency. This work was the catalyst of her passion to to explore more about Data Science. She is skilled in Python, R, statistics and machine learning algorithm. Her final project was to accurately predict the number of taxi pickups that will occur at a specific time and location in New York City, which can inform taxi dispatchers and drivers on where to position their taxies. She implemented and evaluated four different regression models: multiple-linear regression, ridge regression, RandomForest regression and xgboost regression with hyperparameter Bayesian optimization. Furthermore she performed advanced model ensemble techniques with voting classifiers. The final prediction of taxi demand for the incoming week is visualized, summarized and analyzed by the shiny interactive application.