Quantitative Analysis of Commonwealth Edison: Load Prediction and Plant Analysis
This analysis was conducted in Python, particularly Jupyter Notebook. Packages used include: pandas, scipy, seaborn, matplotlib, sklearn and keras. Machine learning was conducted through the keras model (based in TensorFlow), Sequential, to predict load based off of historical data for Commonwealth Edison, a utility company. Plant analysis was conducted through a simple system of loops and development of equations to determine overall cost to run a plant based off of two social cost of carbons. The report details both the process in developing the code, as well as the results. 
Two Mass, Two Spring System 
This analysis was conducted in Matlab. I wrote code that utilized a number of numerical methods and differential equations. When run, the script allows the user to input parameters related to a two-mass two-spring system. The script produces the velocity and displacement graphs for both masses, the power spectrum for the whole system, and an animation showing the predicted motion of the system as a whole. The report includes methodology as well as the entirety of the script.

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