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Leveraging NFL Combine Performance Data: A Machine Learning Analysis
By Cy Seeley, Rohan Madhur, Alexander Mollohan, and Jonathan David Abstract The NFL draft remains an unpredictable process, with teams evaluating a combination of athletic performance, college statistics, and positional value to determine player selections. This study applies machine learning techniques to predict an athlete’s draft position using NFL Combine performance metrics. Employing classification and…
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Unveiling Housing Market Dynamics: A Data-Driven Analysis of Price Trends, Clusters, and Correlations
By Cy Seeley, M.S. Student, Applied Data Science, Syracuse University Abstract This paper explores the dynamics of the U.S. housing market using a large real estate dataset containing over two million records. The analysis delves into housing prices, property sizes, and temporal trends through comprehensive data preparation, statistical methods, and advanced visualization techniques. Findings highlight…
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The Intersection of Fitness Experience, Frequency, and Performance: An Analytical Perspective
By Cy Seeley, M.S. Student, Applied Data Science, Syracuse University Abstract This study examines the relationships between workout habits, individual characteristics, and fitness outcomes among gym members. Using data from 1,000 participants, we investigate the influence of workout type, body fat percentage, experience level, and session duration on key metrics such as calories burned, heart…
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Decoding the Market: Insights into Stock Prices and Trading Patterns
By Cy Seeley, M.S. Student, Applied Data Science, Syracuse University Abstract This study analyzes 846,404 stock trading records to uncover patterns, anomalies, and key insights into market behavior. Metrics such as closing prices, trading volumes, and percentage price changes were examined using statistical and visualization techniques. Key findings include the stock with the highest closing…
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Mental Health Matters: The Role of Emotional Well-being in Academic Performance
By Cy Seeley, M.S. Student, Applied Data Science, Syracuse University Abstract This study investigates the interplay between student behaviors, mental health, and academic performance in college students, using a dataset sourced from Kaggle. Key predictors analyzed include sleep duration, depression status, class engagement, and social interactions. Our findings indicate that students who get more sleep…
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Beyond the Mound: A Statistical Examination of Walk Rates and Predictive Metrics for Pitchers in the 2024 MLB Season
By Cy Seeley, M.S. Student, Syracuse University Abstract This paper investigates the relationship between various pitching metrics and performance outcomes in Major League Baseball (MLB). Specifically, it addresses two critical research questions: (1) Is there a difference in sweet spot percentage allowed between pitchers with high walk rates and those with low walk rates? (2)…
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Exploring Emotional Landscapes: A Comprehensive Analysis of Textual Data Using Machine Learning Models
Cy Seeley, David Caspers, Anshuman Nag Chaudhury, Marianne Lynne Santos Garbo Abstract This study highlights the critical role of emotion analysis in text mining, demonstrating its significant impact across diverse fields such as marketing, politics, and public health. By employing machine learning models, including Naïve Bayes, Support Vector Machines (SVM), and Latent Dirichlet Allocation (LDA),…
Cy's Data Lab
Turning Data Into Knowledge
Cy's Data Lab 