Matching Items (18)

Analysis of Variables in Relation to Dissolved Organic Carbon in Yellowstone National Park

Description

Yellowstone National Park has a vibrant variety of flora, fauna, and hydrothermal systems all collected together in one large and complex system. Studies have been conducted for at least several

Yellowstone National Park has a vibrant variety of flora, fauna, and hydrothermal systems all collected together in one large and complex system. Studies have been conducted for at least several decades in order to make sense of this system in ways that may be relevant to other similar geologies around the world. The latest update in this ever-ongoing study involves the collection and analysis of water samples from 2016. These samples have been analyzed for conductivity, pH, temperature, dissolved organic carbon, dissolved inorganic carbon, carbon isotopes, dissolved oxygen, ferrous iron, sulfide, silica, and more. While not many trends were found in this data in regards to dissolved organic carbon values, this is a substantial addition to a growing body of information that could yield more impressive information in times to come. In addition, factors that have yet to analyzed for this 2016 data, such as concentrations of metals and metalloids, may provide some insights when put through a chloride vs sulfate framework to separate out different reaction regions.

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Date Created
  • 2016-12

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A Novel Historical Safety Metric for Evaluating Road Networks

Description

37,461 automobile accident fatalities occured in the United States in 2016 ("Quick Facts 2016", 2017). Improving the safety of roads has traditionally been approached by governmental agencies including the National

37,461 automobile accident fatalities occured in the United States in 2016 ("Quick Facts 2016", 2017). Improving the safety of roads has traditionally been approached by governmental agencies including the National Highway Traffic Safety Administration and State Departments of Transporation. In past literature, automobile crash data is analyzed using time-series prediction technicques to identify road segments and/or intersections likely to experience future crashes (Lord & Mannering, 2010). After dangerous zones have been identified road modifications can be implemented improving public safety. This project introduces a historical safety metric for evaluating the relative danger of roads in a road network. The historical safety metric can be used to update routing choices of individual drivers improving public safety by avoiding historically more dangerous routes. The metric is constructed using crash frequency, severity, location and traffic information. An analysis of publically-available crash and traffic data in Allgeheny County, Pennsylvania is used to generate the historical safety metric for a specific road network. Methods for evaluating routes based on the presented historical safety metric are included using the Mann Whitney U Test to evaluate the significance of routing decisions. The evaluation method presented requires routes have at least 20 crashes to be compared with significance testing. The safety of the road network is visualized using a heatmap to present distribution of the metric throughout Allgeheny County.

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Date Created
  • 2017-12

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Machine Learning: A Sentiment Analysis of Customer Reviews

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 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.

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Date Created
  • 2020-05

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What Drives Electric Vehicle Sales?

Description

In March 2019, the United Nations Intergovernmental Panel on Climate Change (IPCC) released a report describing the critical importance of the next decade in mitigating the effects of climate change.

In March 2019, the United Nations Intergovernmental Panel on Climate Change (IPCC) released a report describing the critical importance of the next decade in mitigating the effects of climate change. From a consumer perspective, the most impactful method of reducing greenhouse gas emissions is by altering and/or reducing usage of personal and public transportation. Despite the significant technological advances in vehicle electrification, vehicle mileage, and hybrid technology, there is a gap in analysis performed about the relationship between oil prices and electric vehicle sales. This can be largely attributed to the large variation in oil and gas prices within the last decade and the short timeframe in which electric vehicles have been available to the average consumer. In addition to oil prices, significant driving factors of consumer electric vehicle purchases include battery range, availability and accessibly of charging infrastructure, and tax incentives. While consumers clearly have a significant role to play in driving electric vehicle sales, by virtue of the time commitment required to research and develop these emerging technologies, manufacturers have an arguably greater role in determining the market share EVs possess. The concept of “market disruption” versus “market replacement” is an intriguing explanation for the failure of electric vehicles, which as of early 2019 held a market share of less than 2%, to become the primary mode of transportation for most Americans, despite their wide-ranging financial and societal benefits, which will be a key challenge for the industry to overcome in the years to come.

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Date Created
  • 2020-05

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COVID-19’s Effect on the Consumer Shopping Experience at Target

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

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.

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Date Created
  • 2021-05

The Elucidation of Potential New Factors that Influence and Impact Type 2 Diabetes Mellitus Prevalence in Pima Indian populations

Description

Introduction: Diabetes Mellitus (DM) is a significant health problem in the United States, with over 20 million adults diagnosed with the condition. Type 2 Diabetes Mellitus, characterized by insulin resistance,

Introduction: Diabetes Mellitus (DM) is a significant health problem in the United States, with over 20 million adults diagnosed with the condition. Type 2 Diabetes Mellitus, characterized by insulin resistance, in particular has been associated with various adverse conditions such as chronic kidney disease and peripheral artery disease. The presence of Type 2 Diabetes in an individual is also associated with various risk factors such as genetic markers and ethnicity. Native Americans, in particular, are more susceptible to Type 2 Diabetes Mellitus, with Native Americans having over two times the likelihood to present with Type 2 DM than non Hispanic whites. Of worry is the Pima Indian population in Arizona, which has the highest prevalence of Type 2 DM in the world. There have been many risk factors associated with the population such as genetic markers and lifestyle changes, but there has not been much research on the utilization of raw data to find the most pertinent factors for diabetes incidence.

Objective: There were three main objectives of the study. One objective was to elucidate potential new relationships via linear regression. Another objective was to determine which factors were indicative of Type 2 DM in the population. Finally, the last objective was to compare the incidence of Type 2 DM in the dataset to trends seen elsewhere.

Methods: The dataset was uploaded from an open source site with citation onto Python. The dataset, created in 1990, was composed of 768 female patients across 9 different attributes (Number of Pregnancies, Plasma Glucose Levels, Systolic Blood Pressure, Triceps Skin Thickness, Insulin Levels, BMI, Diabetes Pedigree Function, Age and Diabetes Presence (0 or 1)). The dataset was then cleaned using mean or median imputation. Post cleaning, linear regression was done to assess the relationships between certain factors in the population and assessed via the probability statistic for significance, with the exclusion of the Diabetes Pedigree Function and Diabetes Presence. Reverse stepwise logistic regression was used to determine the most pertinent factors for Type 2 DM via the Akaike Information Criterion and through the statistical significance in the model. Finally, data from the Center of Disease Control (CDC) Diabetes Surveillance was assessed for relationships with Female DM Percenatge in Pinal County through Obesity or through Physical Inactivity via simple logistic regression for statistical significance.

Results: The majority of the relationships found were statistically significant with each other. The most pertinent factors of Type 2 DM in the dataset were the number of pregnancies, the plasma glucose levels as well as the Blood Pressure. Via the USDS Data from the CDC, the relationships between Female DM Percentage and the obesity and inactivity percentages were statistically significant.

Conclusion: The trends found in the study matched the trends found in the literature. Per the results, recommendations for better diabetes control include more medical education as well as better blood sugar monitoring.With more analysis, there can be more done for checking other factors such as genetic factors and epidemiological analysis. In conclusion, the study accomplished its main objectives.

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Date Created
  • 2020-05

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The ASU Undergraduate Cookbook: By an ASU Undergraduate Student, For an ASU Undergraduate Student.

Description

This project will be a tribute to my experiences as a person, a chef, and as an ASU student. During my time spent here at ASU I have met a

This project will be a tribute to my experiences as a person, a chef, and as an ASU student. During my time spent here at ASU I have met a diverse group of people that I call my friends. Every time we would spend time together, I would learn about their lives and the experiences they are going through at this university. Everyone I met had a different background, story, and experience. Some of these memorable nights would be spent at my place. Depending on the circumstance, I would cook for my friends, and every time I did, they were amazed by my craft. Growing up, my mother was always working in the realm of fundraising. Through her jobs, we both had the opportunity to meet and work with some of the best chefs the Phoenix valley had to offer. Chefs like Robert Irvine, Mario Batali, Beau MacMillan, Christopher Gross, Michael DiMaria, Eddie Matney, and more. As a child and teenager, my fascination with cooking and food stood out to these figures and many taught me various skills and techniques in the kitchen. I learned to do everything from properly julian tangerines to preparing beef tartar. I even developed from making lemonade on my own when I was two years old to working in a four star restaurant as a line chef at the age of 15. These memories I will be forever grateful for. Through these skills, I have impressed my friends with delicious meals at night. And as we matured through college both in age and living situations, many of my friends have asked to learn from me. The change from freshman dorms to our own houses and townhomes have offered an endless opportunity of options for meals. But, everyone has a different background and skill set when it comes to cooking. A few of my friends have never picked up a knife before and have claimed to “burn water in the microwave.” Others tend to challenge me in preparing meals in their own homes and together we have our own “cookoffs.” From person to person, and living quarter to living quarter, there are many challenges to cooking. This is why I have decided to take the knowledge from my Industrial Engineering classes, my personal cooking skills, and data collected from the student body to create a cookbook for the average ASU student. I plan to include recipes and techniques in the form of Standard Operating Procedures to ensure that the instructions are as easy to follow as they can be. The recipes and techniques I plan to include will encompass data I have collected from the student body. The data will focus around a few key components of any chef and kitchen: tools and appliances available, personal cooking skills, and personal cooking experience. To take on such a challenge, I plan to complete this thesis/creative project in a few direct steps. First and foremost, complete this prospectus (already completed), next, secure funding from ASU for a survey completion incentive. For this survey, I will need a minimum of $250 to distribute between 5 winners. The monetary incentive is to ensure that more than 30 pieces of data (survey responses) are collected from each grade level of students. Next I will send a survey that asks about the aforementioned topics. After the survey is complete, I will collect the data, analyze it, and hone in on the most important and available tools. Finally, I will write stories surrounding my chosen recipes and create said recipes.

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Date Created
  • 2021-05

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Reliance Dashboard: An Automated Real Estate Data Analysis Dashboard

Description

Investment real estate is unique among similar financial instruments by nature of each property's internal complexities and interaction with the external economy. Where a majority of tradable assets are static

Investment real estate is unique among similar financial instruments by nature of each property's internal complexities and interaction with the external economy. Where a majority of tradable assets are static goods within a dynamic market, real estate investments are dynamic goods within a dynamic market. Furthermore, investment real estate, particularly commercial properties, not only interacts with the surrounding economy, it reflects it. Alive with tenancy, each and every commercial investment property provides a microeconomic view of businesses that make up the local economy. Management of commercial investment real estate captures this economic snapshot in a unique abundance of untapped statistical data. While analysis of such data is undeniably valuable, the efforts involved with this process are time consuming. Given this unutilized potential our team has develop proprietary software to analyze this data and communicate the results automatically though and easy to use interface. We have worked with a local real estate property management and ownership firm, Reliance Management, to develop this system through the use of their current, historical, and future data. Our team has also built a relationship with the executives of Reliance Management to review functionality and pertinence of the system we have dubbed, Reliance Dashboard.

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Date Created
  • 2015-05

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The Correlation Between Prevention Levels and Health Status

Description

There is a lot of variation in health outcomes when it comes to individual states in America. Some states, such as Hawaii, have the life expectancy equivalent to that of

There is a lot of variation in health outcomes when it comes to individual states in America. Some states, such as Hawaii, have the life expectancy equivalent to that of developed countries, whereas states like Mississippi have the life expectancy equivalent to that of third world countries. This raised the questions of which states are doing well in health and why, and if their health has to do with their performance in the primary, secondary, tertiary, and/or quaternary prevention levels. The purpose of this research was to investigate if there is a correlation between performance in any of the prevention levels and the overall health status of a state, and if there is, which prevention level would be most beneficial for states to prioritize. The hypothesis of this research was: states that prioritized primary and secondary levels of prevention would have better health than states that prioritized tertiary and quaternary levels of prevention, since basic health measures contribute more to health outcomes than advanced medicine. To investigate this question, indicators were chosen to derive the ranking of each state in health and each of the four prevention levels. Six states were then chosen to represent the high, average, and low health statuses respectively. The six states were ranked for all indicators, and the data was analyzed and compared to determine a potential relationship between the prevention level rankings and the overarching health ranking. It was found that there is a correlation between performance in the primary and secondary prevention levels and a state’s overall health status, whereas there was no such correlation for the tertiary and quaternary levels. A model for health was proposed for states looking to improve their health status, which was to invest in primary prevention, followed by secondary, tertiary, then quaternary prevention and only moving to the next prevention level once the previous level reached a satisfactory threshold.

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Date Created
  • 2021-05

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Digital Transformation and the Self-Driving Car

Description

Digital transformation can be defined as, “the acceleration of business activities, processes, competencies, and models to fully leverage the changes and opportunities of digital technologies and their impact in a

Digital transformation can be defined as, “the acceleration of business activities, processes, competencies, and models to fully leverage the changes and opportunities of digital technologies and their impact in a strategic and prioritized way,” (Edmead, Mark, and IDG Contributor Network, 2016). Following the industrial revolution, digital transformation has taken shape as the current revolution and innovative process. When industry’s and businesses engage in digital transformation, they create disruption and pave the way for enhanced customer value, efficient operational processes, and innovative business models. The prospect of this thesis is to: (1) understand how digital transformation strategy helps to propel innovation for the self-driving car, (2) understand how this innovation will create value in the grand schema for digital transformation, (3) develop a GIS-based (location analytics) study to understand the market opportunity for such technology and innovation. We outline how digital transformation as a whole represents a modern form of creative destruction, that is rewarding to businesses who engage in transformation for efficiency and innovation, and addresses the implications of those that do not. We discuss how digital transformation has affected the auto industry to invest in innovating self-driving cars. Finally, we perform location analytics to develop an opportunity analysis in five big markets around the Phoenix Metropolitan area in the State of Arizona to identify the potential markets for self-driving cars. We conclude this study with a discussion on how technology strategy is transforming the world.

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Date Created
  • 2019-12