• Data cleaning of large health administrative datasets.• Exploration of different analytical models for deep phenotyping and trajectories.• Development of predictive models for diseases of interest using machine learning methods, such as logistic regression, decision tree, support vector machine, and gradient boosting decision tree.

• Built a univariate time-series predictive model that could forecast the future electricity needs from the previous electricity consumption.• Compared and visualized predicting results for various machine learning models.