In the last two decades, fantasy sports have grown massively in popularity. Fantasy football in particular is the most popular fantasy sport in the United States. People spend hours upon hours every year building, researching, and perfecting their teams to compete with others for money or bragging rights. One problem, however, is that National Football League (NFL) players are human and will not perform the same as they did last week or last season. Because of this, there is a need to create a machine learning model to help predict when players will have a tough game or when they can perform above average. This report discusses the history and science of fantasy football, gathering large amounts of player data, manipulating the information to create more insightful data points, creating a machine learning model, and how to use this tool in a real-world situation. The initial model created significantly accurate predictions for quarterbacks and running backs but not receivers and tight ends. Improvements significantly increased the accuracy by reducing the mean average error to below one for all positions, resulting in a successful model for all four positions.

The main purpose of this project is to create a method for determining the absolute position of an accelerometer. Acceleration and angular speed were obtained from an accelerometer attached to a vehicle as it moves around. As the vehicle moves to collect information the orientation of the accelerometer changes, so a rotation matrix is applied to the data based on the angular change at each time. The angular change and distance are obtained by using the trapezoidal approximation of the integrals. This method was first validated by using simple sets of "true" data which are explicitly known sets of data to compare the results to. Then, an analysis of how different time steps and levels of noise affect the error of the results was performed to determine the optimal time step of 0.1 sec that was then used for the actual tests. The tests that were performed were: a stationary test for uses of calibration, a straight line test to verify a simple test, and a closed loop test to test the accuracy. The graphs for these tests give no indication of the actual paths, so the final results can only show that the data from the accelerometer is too noisy and inaccurate for this method to be used by this sensor. The future work would be to test different ways to get more accurate data and then use it to verify this methods. These ways could include using more sensors to interpolate the data, reducing noise by using a different sensor, or adding a filter. Then, if this method is considered accurate enough, it could be implemented into control systems.

Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.