RSS 1.0SUNY Plattsburgh Management Informations Systems and Analytics Student Work
This collection showcases the innovative projects, research, and applied work of students from the Management Information Systems and Analytics (MISA) department of the Business School at SUNY Plattsburgh. It includes a diverse range of contributions such as predictive analytics models, data visualization projects, system design case studies, exploratory data analysis, and capstone projects. The collection highlights students' ability to solve real-world business and technological problems through data-driven methodologies and advanced analytical tools. By sharing their work, this collection aims to inspire collaboration, advance academic inquiry, and demonstrate the practical applications of analytics and MIS in a variety of industries. If you would like to inquire about any of the work featured in this collection, feel free to reach out to the faculty of the MISA Department at SUNY Plattsburgh at sbush010@plattsburgh.edu.
Recent Submissions
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Predicting Customer Churn in the Telecom Sector Using Machine LearningTelecom companies spend a lot to acquire new customers, making it crucial to retain these customers and ensure they continue using their plans. Churn refers to customers leaving their current service provider for another one. Once companies secure a long-term customer, they can profit significantly from them. Therefore, customer churn is a major concern for a company’s revenue and business growth, especially in the competitive telecommunications industry. By analyzing customer behavior and the services they use, companies can predict churn. This predictive capability allows them to minimize losses and enhance their business performance.
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Why Patients Miss Their Appointments, Health Care No-Show AppointmentIn healthcare, extensive data is collected daily to track patient appointments, whether for routine checkups or evaluations of specific symptoms. However, missed appointments are a common issue with significant implications for both patients and healthcare providers. For patients, missed appointments can delay the diagnosis and treatment of health conditions, potentially worsening outcomes. For clinics and hospitals, no-shows lead to inefficient use of resources and lost opportunities to deliver care to other patients who may need it urgently. This study aims to leverage predictive analytics to identify patterns in missed appointments. Specifically, it will explore whether certain health conditions, such as hypertension and diabetes, or demographic factors, including age, gender, and neighborhood, are associated with a higher likelihood of missing appointments. By analyzing these correlations, the study seeks to provide insights that can inform strategies to reduce no-show rates, enhance patient care, and optimize resource allocation within healthcare settings.
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Analyzing Factors Contributing to Pedestrian Fatalities Using Predictive ModelsPedestrian fatalities are a significant issue worldwide, particularly in the United States. These fatalities stem from various causes, including distracted driving, speeding, inadequate road maintenance, and poor visibility due to weather conditions. The risk also varies heavily by area and population density, with high-traffic and high-speed regions often prioritizing vehicle flow over pedestrian safety. The National Highway Traffic Safety Administration (NHSTA) collects extensive accident data involving pedestrians. Using this dataset, we aim to build a classification model to predict the likelihood of one or more fatalities in a traffic accident given key factors like road conditions, weather, time of day, speed and more. The goal of this research is to leverage predictive modeling to identify high-risk situations and develop intervention or fast reaction strategies. By combining NHTSA data with machine learning techniques, this study enhances our understanding of critical risk factors. Furthermore, it explores the potential for practical applications, such as improved road safety tools, safer urban planning, and real-time alert systems.
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Institutional Quality and Foreign Aid Effectiveness: The Future of Corruption and Foreign Aid FlowsThis study examines the relationship between institutional quality and the effectiveness of foreign aid, using corruption as a key indicator. Institutional quality, often emphasized in policy discussions, plays a pivotal role in determining the outcomes of aid initiatives. Previous literature has emphasized the dependency of foreign aid effectiveness on good institutional quality. To evaluate this relationship, a Corruption Index is used as a representative of institutional quality; a factor most talked about in policy making. Using machine learning techniques to analyze whether corruption hinders economic growth and development by undermining the foreign aid effectiveness. Through a panel data approach, aid, foreign direct investment, gross domestic product per capita, and the Corruption Index, are used to predict the future direction of corruption and GDP per capita, which serves as a proxy for the efficiency of foreign aid. The analysis employs multiple machine learning models, with Random Forest and XGBoost demonstrating the highest accuracy. These models suggest that in the future, corruption is likely to decline while GDP per capita is projected to rise, indicating that foreign aid flows may become more effective as corruption diminishes.
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Predicting the Risk of Depression Based on the Patient's Chronic Diseases and Other Physiological AttributesThe prevalence of chronic diseases like diabetes, hypertension, and chronic respiratory conditions contributes significantly to global mortality rates. In addition, there is growing evidence that links mental health disorders to physical activity, body mass index (BMI), and chronic diseases, highlighting their importance in public health research. This study investigates the intricate relationship between chronic diseases and depression, employing innovative Machine Learning techniques to predict depression likelihood based on various health comorbidities. Results indicate that Naive Bayes consistently outperforms other models, highlighting its potential for accurate predictions. The trade-off between specificity and accuracy, however, highlights the necessity of balanced datasets in real-world applications.
