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- Creators: Department of Information Systems
Description
We created an Android application, Impromp2, which allows users to search for and save events of interest to them in the Phoenix area. The backend, built on the Parse platform, gathers events daily using Web services and stores them in a database. Impromp2 was designed to improve upon similarly-purposed apps available for Android devices in several key ways, especially in user interface design and data interaction capability. This is a full-stack software project that explores databases and their performance considerations, Web services, user interface design, and the challenges of app development for a mobile platform.
ContributorsNorth, Joseph Robert (Author) / Balasooriya, Janaka (Thesis director) / Nakamura, Mutsumi (Committee member) / Faucon, Philippe (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
For students on a college campus, many courses can present challenges to them academically. Some universities have taken an initiative to respond to this by offering tutoring opportunities at a central location. Generally this provides help for some struggling students, but others are left with many questions unanswered. Two primary reasons for this are that some tutoring services are broad in scope and that there may not be sufficient one-on-one time with a tutor. With the development of a mobile application, a solution is possible to improve upon the tutoring experience for all students. The concept revolves around the formation of a labor market of freelancers, known as a gig economy, to create a large supply of tutors who can provide their services to a student looking for help in a specific course. A strategic process was followed to develop this mobile application, called Tuzee. To begin, an early concept and design was drafted to shape a clear vision statement and effective user experience. Planning and research followed, where technical requirements including an efficient database and integrated development environment were selected. After these prerequisites, the development stage of the application started and a working app produced. Subsequently, a business model was devised along with possible features to be added upon a successful launch. With a peer-to-peer approach powering the app, monitoring user engagement lies as a core principle for consistent growth. The vision statement will frequently be referred to: enhance university academics by enabling the interaction of students with each other.
ContributorsArcaro, Daniel James (Author) / Ahmad, Altaf (Thesis director) / Sopha, Matthew (Committee member) / Department of Information Systems (Contributor) / WPC Graduate Programs (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The purpose of this thesis was to develop a tool to provide information and data for design teams to use throughout the mobile application design process. Ideally, this would enable teams to see patterns in iterative design, and ultimately use data-driven analysis to make their own decisions. The initial problem was a lack of available information offered by mobile application design teams—the initial goal being to work closely with design teams to learn their decision-making methodology. However, every team that was reached out to responded with rejection, presenting a new problem: a lack of access to quality information regarding the decision-making process for mobile applications. This problem was addressed by the development of an ethical hacking script that retrieves reviews in bulk from the Google Play Store using Python. The project was a success—by feeding an application’s unique Play Store ID, the script retrieves a user-specified amount of reviews (up to millions) for that mobile application and the 4 “recommended” applications from the Play Store. Ultimately, this thesis proved that protected reviews on the Play Store can be ethically retrieved and used for data-driven decision making and identifying patterns in an application’s iterative design. This script provides an automated tool for teams to “put a finger on the pulse” of their target applications.
ContributorsDyer, Mitchell Patrick (Author) / Lin, Elva (Thesis director) / Giles, Charles (Committee member) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
COVID-19 has proved that our society can be adaptable in the most unexpected situations. Chaos and fear struck the nation causing people to react in a variety of ways in an attempt to protect their own self interests. The retail space has had to adjust in large scales, making the shopping experience safer both for the customer and the employees. I was able to experience this first hand at Target, working there many years previous to and during the pandemic, getting to see the shift in consumer patterns. I noticed customers would purchase more products in one department, then the next month it would shift to another department. This paper will analyze those shifts in sales trends both departmentaly and within shopping methods at Target to help identify the largest changes and the possible reasons behind these.
ContributorsSalow, Alexandra (Author) / Byrne, Jared (Thesis director) / Broyles, Katie (Committee member) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
DescriptionI spent two semesters studying and making a Deckbuilding card game. I split my time between researching existing games and playtesting my own. In the end, I produced a fully developed game with printed cards.
ContributorsBarrantes Slivinsky, Andrew (Author) / Loebenberg, Abby (Thesis director) / Mack, Robert (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor)
Created2024-05
Description
Machine learning is the process of training a computer with algorithms to learn from data and make informed predictions. In a world where large amounts of data are constantly collected, machine learning is an important tool to analyze this data to find patterns and learn useful information from it. Machine learning applications expand to numerous fields; however, I chose to focus on machine learning with a business perspective for this thesis, specifically e-commerce.
The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.
The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.
ContributorsMadaan, Shreya (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05