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My assignment analyzes a shoes sales dataset, predicting the likelihood f customers purchasing the shoes. This included online, retail stores, and mall transactions. The analysis identifies patterns in customer purchasing behavior and helps businesses make smarter inventory/marketing decisions. Overall, this assignment demonstrates that machine learning can help companies better understand consumer behavior and optimize clear sales. Pro Point Xtra Credit- Samantha Rogers

In this class, we were given a final project that required us to use our knowledge in JavaScript and HTML with AI to create the code for a Loan Repayment Calculator and our own special purpose calculator. This project was expected to be completed as a one person task. I first completed the Loan Repayment Calculator and was instructed to calculate the amount of money that goes into a loan, the interest percentage, and the monthly payment. After completing that, we were given the choice of creating our calculator of any choice. Since I like plants and the earth, I chose to create a Succulent Longevity Calculator. This calculator uses the following:

• Light Exposure (hours)
• Water (Ounces)
• Temperature (ºF)

The factors listed above calculate how long your succulent will live depending on the controlled circumstances given. If the plant is in conditions that are not suitable for the longevity of its life, there is a message that states that the “Environment [is] not suitable for survival.” On the other hand, if you enter the value zero for any, if not, all of the input boxes, there will be a message that states, “Invalid input. Check your values.”

I created this calculator because anyone can use it for their own personal growth in their green thumb journey and because I truly do love succulents and creativity.

Project URL: https://misdemo.temple.edu/tut26379c/project2/

As part of MIS 2402 (Web Application Development) at Temple University, taught by Professor Laurel Miller, I used Anthropic’s Claude as an AI development tool to design and build a functional web application from scratch. The project required prompting the AI to generate a single-file web application using HTML, CSS, JavaScript, jQuery, and Bootstrap — then deploying it to a live server via SSH (Secure Shell). For this project, I independently conceived a freelance rate calculator: a tool that takes a freelancer’s target income, business expenses, work schedule, and billable-hour percentage, and computes the minimum hourly rate required to meet their financial goals. My role was to define the requirements, verify the mathematical accuracy of the output against manual calculations, and confirm the application handled invalid input correctly before deployment. The project gave me hands-on experience using AI tools in a professional software development workflow, as well as practical exposure to web technologies and server-side file management.

This application can be found at:
Freelance Rate Calculator

“The goal of this project is to provide MIS students with additional hands-on experience in data
analysis and reinforce the concepts and methods covered in class.”

Created a decision tree through python using a selected data set (from Kaggle). Described the data set and how it can be useful for certain occupations. Adjusted the data and minimum split to better suit my desired outcome. Noted the highest and lowest probability. Used prediction analysis to provide at least 4 examples of data points. Learned how to properly analyze a data set.

For this project in MIS2502, I used a dataset about teen mental health to predict if someone is depressed or not. I built a decision tree model in JupyterLab and looked at things like social media use, sleep, stress, and anxiety. My role was to clean the data, run the model, and look at the results. I learned how to use decision trees on real data and how different factors can affect mental health. This project helped me get better at data analysis and using Python.

“The goal of this project is to provide MIS students with additional hands-on experience in data
analysis and reinforce the concepts and methods covered in class.”

Created a decision tree through python using a selected data set (from Kaggle). Described the data set and how it can be useful for certain occupations. Adjusted the data and minimum split to better suit my desired outcome. Noted the highest and lowest probability. Used prediction analysis to provide at least 4 examples of data points. Learned how to properly analyze a data set.

For my Spotify Decision Tree points project, I took a large-scale Spotify dataset and used a decision tree to figure out what makes a song danceable based on its audio features. By applying predictive analytics to attributes like valence, tempo, and acousticness, I achieved a consistent classification accuracy of 75%. This project provided valuable insight into how data science can be leveraged within the music industry to identify the specific technical characteristics that drive listener engagement.

For my MIS 2402 final project at Temple University, I built a two-part financial web application using HTML, CSS, Bootstrap, and JavaScript with the assistance of AI. Part 1 is a loan repayment calculator, and Part 2 is a custom options break-even and P&L calculator. Through this project, I learned how to collaborate with AI tools to bring real-world financial concepts to life as interactive web apps.

My group and I created a powerpoint that showed what pitchers needed to be cut from the phillies 2025 roster based on key stats from the 2025 season. The main statistics we analyzed through tableau workbooks were Whip and WAR. Group Project

MIS2502-PRO_Points_Assignment (1)

I undertook an independent data analysis project using Decision Tree classification on a real medical diagnostic data set that was not covered in the course. This project aimed to create a classification model to diagnose tumors as malignant or benign using clinical metrics of cell nuclei. I conducted hyperparameter tuning tests with various ‘minimum sample split’ values to reduce overfitting on the training data to achieve the best accuracy of 93.9% on the test data, using a value of 20. This project illustrates how predictive analytics can be used to enhance diagnostic accuracy in medicine.