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When it comes to registering to vote, Hispanic voters can only register as “Hispanic” in the “Race/ Ethnicity” category, causing difficulties when analyzing voting trends amongst the Hispanic community. Upon the recent idea that not all Hispanic Groups vote the same, the goal is to create a model that can possibly identify a voter’s Hispanic Group with the information provided on the public Florida voter file. This is accomplished using name and zip code data for all voters in Palm Beach, Florida. This paper will explore the model implemented, its findings and limitations. Palm Beach, Florida, is met with low confidence in classification, leaving the final sample of highly confident active Hispanic voters with 15% of its original sample. Further analysis on other counties will be needed to gauge how impactful this limitation might be on the rest of the state. 

Neural networks are an emerging topic in the data science industry due to their high versatility and efficiency with large data sets. Past research has utilized machine learning on experimental data in the material sciences and chemistry field to predict properties of metal oxides. Neural networks can determine underlying optical properties in complex images of metal oxides and capture essential features which are unrecognizable by observation. However, neural networks are often referred to as a “black box algorithm” due to the underlying process during the training of the model. This poses a concern on how robust and reliable the prediction model actually is. To solve this ensemble neural networks were created. By utilizing multiple networks instead of one the robustness of the model was increased and points of uncertainty were identified. Overall, ensemble neural networks outperform singular networks and demonstrate areas of uncertainty and robustness in the model. 

An analysis was done to find the emerging location(s) for the electrical vertical takeoff and landing (eVTOL) industry. The VTOL and eVTOL industries are aiming to replace short private jet travel as helicopters will have the ability to cut out some of the driving. For instance, it can bring a client to the top of a skyscraper because heliports and vertiports can be positioned almost anywhere making them more accessible than private jets. Initially, a numerical analysis was done to see how the past could predict the future, which showed electric vehicle (eV) charging stations have been exponentially rising while heliports have been declining. This was used in the analysis when choosing the criteria to analyze. It was determined that the final recommendation for the emerging location of the eVTOL industry would use the following: 25% Flexjet data, 25% eV charging stations, 25% population density, and 25% median household income. The Flexjet flight data reflected the busiest airports for flights 30 minutes or less for departures and arrivals. The eV charging station data showed which states have the largest number of charging stations at parking lots or garages, since they can be converted to vertiports in the future. The population density and median household income showed the top 10 cities in the US for each, respectively. This led to a final score given for every state and showed New York would be the emerging location for the eVTOL industry based on the data and scoring. This led to recommendations given to OneSky for New York. 

An experiment was performed to investigate a modified pooling method for use in convolutional neural networks for image recognition. This algorithm–Variable Stride–allows the user to segment an image and change the amount of subsampling in each region. This control allows for the user to maintain a higher amount of data retention in more important regions of the image, while more aggressively subsampling the less important regions to increase training speed. Three Variable Stride methods were compared to the preexisting pooling algorithms, Maximum Pool and Average Pool, in three different network configurations tasked with classifying Diabetic Retinopathy images between its early and advanced stages. Each combination was run multiple times and the AUC, Validation Loss, Validation Accuracy, and number of training epochs until convergence of each run was all collected. Maximum Pool and Average Pool were both found to be superior to Variable Stride when deployed in these scenarios.