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Senior Capstone Project Presentation – April 2020

Project Title: In Depth Visual Analysis on Creating Safer Streets in DC
Name of Student: Kendra Atchison
Faculty Advisor: Dr. Dong Jeong

Project Abstract Traffic and pedestrian fatalities in D.C. are consistently increasing. Finding ways to decrease the number of fatalities and accidents is an integral part in keeping the city safe. Since car crash data provides invaluable information to improve our community, understanding the car crash data collected by D.C. is performed. By analyzing the data to identify high pedestrian and driver accident rates in each street and geographical region, it is possible to reduce the number of fatalities and collisions. For the analysis, a visualization tool such as Tableau is utilized to show trends within the city and what can be done to improve the number of fatalities that occur on a daily basis.

Project Title Visual Air Quality Data Analysis
Name of Students: Olivia Nkweto Katebe, Stephane Nguemengne Sipa
Faculty Advisor: Dr. Dong Jeong

Project Abstract Understanding air quality is important because it is closely connected to human health issues. This project focuses on performing a visual analysis on understanding the trends of air quality in D.C. for four decades. The air quality dataset from 1980 to 2019 from the U.S. Environmental Protection Agency is used. Since the dataset provides seven chemical components as the evidence of the air quality in the region such as CO, SO2, Lead, NO2, Ozone, PM10, PM2.5 it is important to perform various trend analysis. With a visual analytics tool (called Tableau), several visualization techniques are utilized to conduct the analysis.

Project Title BigSkinny: Designing an eCommerce Web Application
Name of Student Demond Sweet
Faculty Advisor: Dr. Dong Jeong

Project Abstract While it is possible for shoppers to find clothes using the world wide web, it is occasionally problematic for non-traditional sized people. Online availability of larger sized clothes lacks style and creativity when compared to smaller sized clothes offered by many other online retailers. It is rare to find large sized online clothing retailers that encompass the latest fashion and design trends. This project is an academic opportunity to leverage technology and business by implementing an eCommerce web application that incorporates a user-friendly design, for shoppers of all shapes and sizes. This application will allow shoppers to view and purchase available clothing. Additionally, a back-end monitory system will monitor available inventories and update changes in the inventories automatically.

Project Title VAST Challenge2019: Data Analysis of the Disaster at St. Himark
Name of Student Gerson Escobar
Faculty Advisor: Dr. Dong Jeong

Project Abstract This project focuses on solving the problem introduced at VAST Challenge2019. The main idea of the challenge is to analyze a fake earthquake dataset in the area of St. Himark. The dataset includes seismic readings, damage reporting, and initial deployment data. For analyzing the dataset, a visual analysis is utilized to analyze and compare the seismic readings of the quake, responses from the community, and background knowledge of the city. With this approach, it is possible to help triage their efforts for rescue and recovery.

Project Title Car Value Prediction Model
Name of Student Abnezer Tefera
Faculty Advisor: Dr. Dong Jeong

Project Abstract Purchasing a car is a major investment for most people. However, it is considered as a depreciating asset. In order to minimize the amount of money lost over the lifetime of the vehicle, one big factor to take into consideration at the point of sale is the potential depreciation of the car. Since there are numerous factors that make vehicles’ values depreciate at different rates. This project utilizes machine learning algorithms to precisely predict the depreciation rates for known vehicles appeared in truecar.com. The outcomes of this project will provide reliable information to help buyers make decisions on purchasing vehicles.