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Articles 1 - 30 of 42
Full-Text Articles in Business
Predictive Analysis Of Local House Prices: Leveraging Machine Learning For Real Estate Valuation, Joey Hernandez, Danny Chang, Santiago Gutierrez, Paul Huggins
Predictive Analysis Of Local House Prices: Leveraging Machine Learning For Real Estate Valuation, Joey Hernandez, Danny Chang, Santiago Gutierrez, Paul Huggins
SMU Data Science Review
This paper presents a comprehensive study examining the real estate market potential in the dynamic urban landscapes of Frisco and Plano, Texas. Combining traditional real estate analysis with cutting-edge machine learning techniques, the study aims to predict home prices and assess investment feasibility. Leveraging these findings, the study proposes a strategic focus on predictive modeling and investment potential identification, emphasizing the continual refinement of machine learning models with updated data to accurately forecast changes in the real estate market. By harnessing the predictive power of these models, investors can identify high-growth areas and optimize their investment decisions, thus capitalizing on …
Enhancing Customer Support Operations Through Gpt & Q-Learning: A Model Study, Adam Alidra, Bob O'Brien, Dalton Young
Enhancing Customer Support Operations Through Gpt & Q-Learning: A Model Study, Adam Alidra, Bob O'Brien, Dalton Young
SMU Data Science Review
Abstract. “Growth strategies that are purpose-led, customer-centric, experience-driven, data/AI-enabled, and technology-scaled require new mindsets…” (Cornfield, 2021). What can we take from this? Business growth and customer experience are inextricably tied together. Therefore, thriving, as an organization, is dependent on reimagining enterprise operations through modern, scalable data and AI technologies. Our study aims to enhance support operations with emerging AI capabilities, including OpenAI’s LLM models, built on self-attention mechanism transformer architecture, and tailored for business needs through prompt engineering. Our research uses Markov Decision Process and the Q-learning algorithm to evaluate synthetically created support incidents. Through this set of methods, our …
Personalization: University Fundraising, Jasmine O'Neal, Akib Hossain, Yogesh Bhalerao
Personalization: University Fundraising, Jasmine O'Neal, Akib Hossain, Yogesh Bhalerao
SMU Data Science Review
University fundraising campaigns, which are typically a multi-year endeavor, help institutions build a strong financial foundation to enable a unique student experience. At the heart of effective fundraising is having strong relationships and partnerships with certain audiences that provide the best donation opportunities. Generally, alumni networks and the local community makeup these audiences as strong fundraising bases. Therefore, universities have a significant responsibility in developing these relationships to gain deeper insights into their constituents' needs and interests. This enables them to tailor effective engagement strategies, thereby increasing the likelihood of meeting donation expectations. This research explores how machine learning techniques …
Investigation Into A Practical Application Of Reinforcement Learning For The Stock Market, Philip Traxler, Sadik Aman, Will Rogers, Allyn Okun
Investigation Into A Practical Application Of Reinforcement Learning For The Stock Market, Philip Traxler, Sadik Aman, Will Rogers, Allyn Okun
SMU Data Science Review
A major problem of the financial industry is the ability to adapt their trading strategies at the same rate the market evolves. This paper proposes a solution using existing Reinforcement Learning libraries to help find new strategies at a practical scale. Using a wide domain of ticker symbols, an algorithm is trained in an environment that better represents reality. The supplied decision-making algorithm is tested using recorded data from the U.S stock market from 2000 through 2022. The results of this research show that existing techniques are statistically better than making decisions at random. With this result, this research shows …
Forecasting Accessory Demand In The Automotive Industry, Eric Cadena, Kevin Albright, Harry Wang, Satvik Ajmera
Forecasting Accessory Demand In The Automotive Industry, Eric Cadena, Kevin Albright, Harry Wang, Satvik Ajmera
SMU Data Science Review
The automotive industry seeks effective ways to forecast consumer demand to avoid overstocking, waste, underproduction, and employee underperformance. Modeling future demand for vehicles is standard, however parts & accessories are a significant subset of overall automotive revenue. There is no industry standard for predicting the quantity of accessories sold or revenue. This paper seeks to use the best industry forecasting methods and research practices to build a predictive model that forecasts vehicle accessory sales. The time-series forecasting model utilizes Toyota Motor Corporation data in a first attempt to predict accessory sales.
Bridging The Chasm Between Fundamental, Momentum, And Quantitative Investing, Allen Hoskins, Jeff Reed, Robert Slater
Bridging The Chasm Between Fundamental, Momentum, And Quantitative Investing, Allen Hoskins, Jeff Reed, Robert Slater
SMU Data Science Review
A chasm exists between the active public equity investment management industry's fundamental, momentum, and quantitative styles. In this study, the researchers explore ways to bridge this gap by leveraging domain knowledge, fundamental analysis, momentum, crowdsourcing, and data science methods. This research also seeks to test the developed tools and strategies during the volatile time period of 2020 and 2021.
Following The Crowd: Beginners Investors Guide To The Options Market, Jeremy Dawkins, Alexy Morris, Jacob Gipson, Masoud Valizadeh
Following The Crowd: Beginners Investors Guide To The Options Market, Jeremy Dawkins, Alexy Morris, Jacob Gipson, Masoud Valizadeh
SMU Data Science Review
While the options market may be intimidating for a beginner, having the right tools can help improve the outcome of their investments. This project aims to develop a tool that uses time-series analysis and forecasting to model the future demand of S&P 500 and AAPL options contracts. The open interest of these contracts will be analyzed using various models such as AR, ARIMA, Neural Networks, and VAR, along with the put-call ratio. The goal is not to make buy or sell recommendations, but alert the user when money is flowing into a security or index. Of all the models, the …
Profiting From Dow Jones Industrial Index And Hang Seng Index Using Moving Average And Macd Optimization Model, Anthony Yeung, Joe Wailun Chung, Nibhrat Lohia, Onyeka Emmanuel
Profiting From Dow Jones Industrial Index And Hang Seng Index Using Moving Average And Macd Optimization Model, Anthony Yeung, Joe Wailun Chung, Nibhrat Lohia, Onyeka Emmanuel
SMU Data Science Review
Before the internet, high-speed laptop computers, and big data became accessible and popular, academia on stock market trading concentrated on Efficient Market Hypothesis (EMH). EMH hinges on the idea that the market is efficient and there is no extra return that could be generated. With the dynamic development of the internet, big-data and computing technology, many researchers started to pay attention to Technical Analysis and its usage. Numerous academic papers claimed that technical analysis can enhance returns by using various technical tools. This paper explores in-depth the simulation model of Moving Average and Moving Average Convergence/Divergence (MACD) to come up …
Fraud Pattern Detection For Nft Markets, Andrew Leppla, Jorge Olmos, Jaideep Lamba
Fraud Pattern Detection For Nft Markets, Andrew Leppla, Jorge Olmos, Jaideep Lamba
SMU Data Science Review
Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based …
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
SMU Data Science Review
Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …
Analysis Of First-Time Completion In The Field Service Environment, Gavin Rick, Scott Englerth, Marc Carter, Hayley Horn
Analysis Of First-Time Completion In The Field Service Environment, Gavin Rick, Scott Englerth, Marc Carter, Hayley Horn
SMU Data Science Review
First-time completion is an important measure of service quality and efficiency in the field service industry. Customers call upon field service providers to repair their equipment in a timely manner so it can be put back into service for their business demands. Responsiveness can be measured through first-time completion and is defined as completing the repair on the first visit of a service call. This research is exploring the first-time completion in the forklift service industry. This research found the primary factors that impact first-time completion percentage in this industry include parts on hand, parts backorder process, technician experience, and …
A Machine Learning Approach To Revenue Generation Within The Professional Hair Care Industry, Alexander K. Sepenu, Linda Eliasen
A Machine Learning Approach To Revenue Generation Within The Professional Hair Care Industry, Alexander K. Sepenu, Linda Eliasen
SMU Data Science Review
The cosmetic and beauty industry continues to grow and evolve to satisfy its patrons. In the United States, the industry is heavily science-driven, innovative, and fast-paced, suggesting that to remain productive and profitable, companies must seek smart alternatives to their current modus operandi or risk losing out on this multi-billion-dollar industry to fierce competition. In this paper, the authors seek to utilize machine learning models such as clustering and regression to improve the efficiency of current sales and customer segmentation models to help HairCo (pseudonym for confidentiality), a professional hair products manufacturer, strategize their marketing and sales efforts for revenue …
Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun
Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun
SMU Data Science Review
This study investigates a comparison of classification models used to determine aspect based separated text sentiment and predict binary sentiments of movie reviews with genre and aspect specific driving factors. To gain a broader classification analysis, five machine and deep learning algorithms were compared: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network Long-Short-Term Memory (RNN LSTM). The various movie aspects that are utilized to separate the sentences are determined through aggregating aspect words from lexicon-base, supervised and unsupervised learning. The driving factors are randomly assigned to various movie aspects and their impact tied to …
Spotify: You Have A Hit!, Christopher E. Dawson Jr., Steve Mann, Edward Roske, Gauthier Vasseur
Spotify: You Have A Hit!, Christopher E. Dawson Jr., Steve Mann, Edward Roske, Gauthier Vasseur
SMU Data Science Review
Abstract. Over 87% of the streaming music is owned by four major record labels (Jones, 2018). Yet, the songs owned by those labels account for <1% of the total amount of music created each year. These labels are historically better at identifying talent (though this talent identification is becoming more difficult). Even though Spotify has 36% of the streaming marketing share (T4, 2021), Spotify has not been profitable because of the large licensing costs paid to the large music labels. If Spotify could identify hit songs & artists before the large labels, they would sign those artists and dramatically reduce their licensing costs. Using the Spotify API, this paper will use Spotify data on over 400K songs over the last three years for exploratory data analysis, provide descriptive statistics, perform feature selection, and develop models using LASSO and XGBOOST Classification. The research determined multiple key features and predicted with over 60% accuracy songs which were going to be a hit (defined as >90% popularity).
Enhanced Data Science Methods For Freight Optimization At Kelly-Moore Paints, Lance Dacy, Reannan Mcdaniel, Shawn Jung
Enhanced Data Science Methods For Freight Optimization At Kelly-Moore Paints, Lance Dacy, Reannan Mcdaniel, Shawn Jung
SMU Data Science Review
Kelly-Moore Paints is a paint manufacturing company founded in San Carlos, California in 1946 by William Kelly and William Moore. It has stores located in California, Texas, Oklahoma, and Nevada. They currently own 11 42’ trailers, contract 4 distinct drivers, and service 44 stores Monday-Thursday from its Texas Distribution and Manufacturing Center in Hurst, TX. Given that transportation costs are typically the highest in the supply chain costs, this study will employ data science techniques to ensure the transportation routing, store ordering mechanism, and trailer utilization are at the best efficiency possible given the current ordering patters of the stores. …
Modeling And Application Of Neural Networks For Automotive Damage Appraisals, Fred Poon, Yang Zhang, Jonathon Roach, David Josephs, John Santerre
Modeling And Application Of Neural Networks For Automotive Damage Appraisals, Fred Poon, Yang Zhang, Jonathon Roach, David Josephs, John Santerre
SMU Data Science Review
The automotive damage appraisal process is one of the areas in property and casualty insurance that can benefit from applying deep learning technology and computer vision. It is commercially beneficial to introduce a fast and efficient claim process that can shorten the entire process. Technologies adopted include advanced neural network algorithm and Mask R-CNN to solve tasks such as image classification, object detection, and segmentation in combination with statistical analysis and model construction of the appraisal metadata to approximate final claim cost. With a database of over 3 million records as the data source, a workflow is constructed via a …
Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman
Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman
SMU Data Science Review
Much progress has been made in text analysis, specifically within the statistical domain of Term Frequency (TF) and Inverse Document Frequency (IDF). However, there is much room for improvement especially within the area of discovering Emerging Trends. Emerging Trend Detection Systems (ETDS) depend on ingesting a collection of textual data and TF/IDF to identify new or up-trending topics within the Corpus. However, the tremendous rate of change and the amount of digital information presents a challenge that makes it almost impossible for a human expert to spot emerging trends without relying on an automated ETD system. Since the U.S. Government …
Analysis Of The Commercial Real Estate Market In A Post Covid-19 World, Brandon Croom, Sean Kennedy, Sandesh Ojha, Justin Sparks
Analysis Of The Commercial Real Estate Market In A Post Covid-19 World, Brandon Croom, Sean Kennedy, Sandesh Ojha, Justin Sparks
SMU Data Science Review
The volatility in the commercial real estate market has been greatly influenced by the new societal practices brought about by the COVID-19 pandemic. The COVID-19 pandemic has added additional factors to already complex modeling to value and predict commercial real estate prices. Although multiple methodologies have been applied to commercial real estate valuation, these methods have not yet taken the COVID-19 pandemic factor into account. The main contribution of this article lies in developing an application for commercial real estate valuation which includes the COVID-19 pandemic factor. Thought this article a Hedonic model was developed to compare the impacts of …
Price Optimization For Revenue Maximization At Scale, Nikhil Gupta, Massimiliano Moro, Kailey A. Ayala, Bivin Sadler
Price Optimization For Revenue Maximization At Scale, Nikhil Gupta, Massimiliano Moro, Kailey A. Ayala, Bivin Sadler
SMU Data Science Review
This study presents a novel approach to price optimization in order to maximize revenue for the distribution market of non-perishable products. Data analysis techniques such as association mining, statistical modeling, machine learning, and an automated machine learning platform are used to forecast the demand for products considering the impact of pricing. The techniques used allow for accurate modeling of the customer’s buying patterns including cross effects such as cannibalization and the halo effect. This study uses data from 2013 to 2019 for Super Premium Whiskey from a large distributor of alcoholic beverages. The expected demand and the ideal pricing strategy …
Bert For Question Answering On Bioasq, Eric R. Fu, Rikel Djoko, Maysam Mansor, Robert Slater
Bert For Question Answering On Bioasq, Eric R. Fu, Rikel Djoko, Maysam Mansor, Robert Slater
SMU Data Science Review
Machine reading comprehension and question answering are topics of considerable focus in the field of Natural Language Processing (NLP). In recent years, language models like Bidirectional Encoder Representations from Transformers (BERT) [3] have been very successful in language related tasks like question answering. The difficulty of the question answering task lies in developing accurate representations of language and being able to produce answers for questions. In this study, the focus is to investigate how to train and fine tune a BERT model to improve its performance on BioASQ, a challenge on large scale biomedical question answering. Our most accurate BERT …
Automated Interactive 3d Geospatial Data Assimilation, Formatting And Visualization System For Development Of Subsurface Conceptual Site Models, Aaron Cattley, Gavin Hudgeons, Bruce Lee
Automated Interactive 3d Geospatial Data Assimilation, Formatting And Visualization System For Development Of Subsurface Conceptual Site Models, Aaron Cattley, Gavin Hudgeons, Bruce Lee
SMU Data Science Review
The natural evolution of the collection and storage of sub- surface data in Texas has resulted in the current state where data for certain resources, such as water resources, have not been assimilated with state oil and gas and injection data in a meaningful way that allows for rapid understanding and data analysis for a physical land site. The consequences that result due to data from different spheres not being in sync are often duplication of work being performed but not in a consistent manner. However, the reality is that the infrastructure and impacts of these sectors are deeply intertwined. …
Predicting Attrition - A Driver For Creating Value, Realizing Strategy, And Refining Key Hr Processes, Kevin Mendonsa, Maureen Stolberg, Vivek Viswanathan, Scott Crum
Predicting Attrition - A Driver For Creating Value, Realizing Strategy, And Refining Key Hr Processes, Kevin Mendonsa, Maureen Stolberg, Vivek Viswanathan, Scott Crum
SMU Data Science Review
Talent is the most important asset for every organization's success. While attrition (or churn) and turnover can refer to both employees and customers, this paper will focus on employee attrition only. Many organizations accept attrition as an inevitable cost of doing business and do nothing to adopt or implement mitigating strategies to combat it. World class companies on the other hand take deliberate measures to understand, control and mitigate attrition (turnover) at every stage. Unmitigated attrition can have a devastating effect on an organization's bottom line and market value. In addition, the “invisible" costs of low employee morale, reduced employee …
Toxic Comment Classification, Sara Zaheri, Jeff Leath, David Stroud
Toxic Comment Classification, Sara Zaheri, Jeff Leath, David Stroud
SMU Data Science Review
This paper presents a novel application of Natural Language Processing techniques to classify unstructured text into toxic and non- toxic categories. In the current century, social media has created many job opportunities and, at the same time, it has become a unique place for people to freely express their opinions. Meanwhile, among these users, there are some groups that are taking advantage of this framework and misuse this freedom to implement their toxic mindset (i.e. insulting, verbal sexual harassment, threads, Obscene, etc.). The 2017 Youth Risk Behavior Surveillance System (Centers for Disease Control and Prevention) estimated that 14.9% of high …
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, Tanvi Arora, Rajat Chandna, Stacy Conant, Bivin Sadler, Robert Slater
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, Tanvi Arora, Rajat Chandna, Stacy Conant, Bivin Sadler, Robert Slater
SMU Data Science Review
In this paper, historical data from a wholesale alcoholic beverage distributor was used to forecast sales demand. Demand forecasting is a vital part of the sale and distribution of many goods. Accurate forecasting can be used to optimize inventory, improve cash ow, and enhance customer service. However, demand forecasting is a challenging task due to the many unknowns that can impact sales, such as the weather and the state of the economy. While many studies focus effort on modeling consumer demand and endpoint retail sales, this study focused on demand forecasting from the distributor perspective. An ensemble approach was applied …
Demand Forecasting For Alcoholic Beverage Distribution, Lei Jiang, Kristen M. Rollins, Meredith Ludlow, Bivin Sadler
Demand Forecasting For Alcoholic Beverage Distribution, Lei Jiang, Kristen M. Rollins, Meredith Ludlow, Bivin Sadler
SMU Data Science Review
Forecasting demand is one of the biggest challenges in any business, and the ability to make such predictions is an invaluable resource to a company. While difficult, predicting demand for products should be increasingly accessible due to the volume of data collected in businesses and the continuing advancements of machine learning models. This paper presents forecasting models for two vodka products for an alcoholic beverage distributing company located in the United States with the purpose of improving the company’s ability to forecast demand for those products. The results contain exploratory data analysis to determine the most important variables impacting demand, …
Qlime-A Quadratic Local Interpretable Model-Agnostic Explanation Approach, Steven Bramhall, Hayley Horn, Michael Tieu, Nibhrat Lohia
Qlime-A Quadratic Local Interpretable Model-Agnostic Explanation Approach, Steven Bramhall, Hayley Horn, Michael Tieu, Nibhrat Lohia
SMU Data Science Review
In this paper, we introduce a proof of concept that addresses the assumption and limitation of linear local boundaries by Local Interpretable Model-Agnostic Explanations (LIME), a popular technique used to add interpretability and explainability to black box models. LIME is a versatile explainer capable of handling different types of data and models. At the local level, LIME creates a linear relationship for a given prediction through generated sample points to present feature importance. We redefine the linear relationships presented by LIME as quadratic relationships and expand its flexibility in non-linear cases and improve the accuracy of feature interpretations. We coin …
Quantitative Model For Setting Manufacturer's Suggested Retail Price, Peter Byrd, Jonathan Knowles, Dmitry Andreev, Jacob Turner, Brian Mente, Laroux Wallace
Quantitative Model For Setting Manufacturer's Suggested Retail Price, Peter Byrd, Jonathan Knowles, Dmitry Andreev, Jacob Turner, Brian Mente, Laroux Wallace
SMU Data Science Review
In this paper, we present a quantitative approach to model the manufacturer’s suggested retail price (MSRP) for children’s doll- houses and establish relationships among key features that contribute most to establishing MSRP. Determination of the MSRP is a critical step in how consumers respond with their wallets when purchasing an item. KidKraft, a global leader in toys and juvenile products, sets MSRP subjectively using product experts. The process is arduous and time consuming requiring the focus of specialized resources and knowledge of the interaction between key attributes and their impact on consumer value. An accurate prediction of MSRP during the …
Identifying At-Risk Clients For Xyz Packaging, Co., Eduardo Carlos Cantu Medellin, Mihir Parikh, Christopher Graves, Brendon Jones
Identifying At-Risk Clients For Xyz Packaging, Co., Eduardo Carlos Cantu Medellin, Mihir Parikh, Christopher Graves, Brendon Jones
SMU Data Science Review
We present a multi-algorithmic modeling approach for the identification of at-risk customers for XYZ Packaging Inc. We define at-risk customers as those having declining seasonally adjusted gross income forecasts which are a strong indicator of impending customer churn. Customer retention is an area of interest regardless of industry but is especially vital in commodity-based low margin industries. We employ traditional Autoregressive Integrated Moving Average (ARIMA) and Anomaly Detection algorithms for discriminating changes in customer revenue patterns. Ultimately, we identify a meaningful proportion of clients whose forward-looking quarterly demand can be predicted within an actionable degree of accuracy.
Optimize The Effectiveness Of Recruiting Campaigns, Ryan A. Talk, Lakshmi Bobbillapati, Marshall Coyle
Optimize The Effectiveness Of Recruiting Campaigns, Ryan A. Talk, Lakshmi Bobbillapati, Marshall Coyle
SMU Data Science Review
Abstract. Recruiting marketing plays an important role in the talent acquisition strategy today. To find the best candidates, companies make substantial investments through numerous recruiting agencies, job boards, and internal systems such as Indeed, LinkedIn, Monster, Talent Communities. In this paper we obtained a company’s LinkedIn Job Posting data to try to predict the number of visits they will receive for each job posting based on the time of the year it is posted. We compare AR(1), AR(2), AR(52), MA(1), and ARMA(1, 1) time series methods to a baseline of a persistence model. We found that out of these 5 …
Dare To Venture: Data Science Perspective On Crowdfunding, Ruhaab Markas, Yisha Wang
Dare To Venture: Data Science Perspective On Crowdfunding, Ruhaab Markas, Yisha Wang
SMU Data Science Review
Crowdfunding is an emerging segment of the financial sectors. Entrepreneurs are now able to seek funds from the online community through the use of online crowdfunding platforms. Entrepreneurs seek to understand attributes that play into a successful crowdfunding project (commonly known as campaign). In this paper we seek so understand the field of crowdfunding and various factors that contribute to the success of a campaign. We aim to use traditional modeling techniques to predict successful campaigns for Kickstarter. We find emerging field of crowdfunding has many nuances due to various funding methods of online platforms. The importance of having relevant …