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- Creators: Barrett, The Honors College
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.
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.
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.
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.
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.