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I imported a CSV file containing data from a bank and used Python in Jupiter Notebook to train a model that predicts customer outcomes. I produced a decision tree that evaluated the likelihood that a customer would leave the bank based on numerous demographics and metrics.

This project focuses on using a decision tree algorithm to analyze the salaries of NBA players based on their performance data. The main goal is to predict player salaries using key features such as their age, average minutes played, and win shares. By analyzing these features, we can understand how player performance impacts their earnings.

The project involves building and optimizing a decision tree model. First, we tested different values for the minimum split parameter to find the best balance between overfitting and underfitting. Then, we identified the highest and lowest salary nodes in the tree and analyzed the features that influenced them. Finally, we used the model to predict salaries for selected player data points, demonstrating how decision trees can provide meaningful insights into salary structures.

This project involved analyzing the 2025 IPL Auction dataset using Decision Tree modeling to predict whether a player is “Capped” or “Uncapped” based on their base price and winning bid. The analysis was conducted as part of an academic assignment for MIS2502, focused on applying data analysis techniques to a new dataset.

My role included data preprocessing, building the decision tree model, and extracting meaningful insights. I consolidated multiple team-specific datasets into a unified format, selected relevant features, and implemented the model using Python and Jupyter Notebook. Key deliverables included a detailed Word document summarizing the findings, a visual representation of the decision tree, predictions for test examples, and the Jupyter Notebook used for the analysis.

Through this activity, I developed a deeper understanding of Decision Tree algorithms, feature selection, and the trade-offs involved in tuning model parameters like minimum split. This hands-on experience reinforced my ability to handle real-world datasets, draw insights from data, and effectively communicate results through structured documentation and visualizations.

I analyzed a data set containing the sex, class, age, fare price, etc. of passengers on the titanic to predict whether or not they survived. I used a decision tree in Jupyter Notebook to classify the data and deciphered the best minimum split. Then I drew conclusions about the survivability of different passengers based on the factors detailed in the tree. Through this project I was able to further my understanding on decision tree analysis and apply it to a topic of my own interest.

Using a data set that gave person and whether they qualified for a loan. The variables included were a person’s age, income, credit score, gender, occupation, education level, and marital status. I found the best value for the minimum split and the highest using the decision tree algorithm; and also found the lowest node probability. Lastly, I used the four examples from the dataset and predicted whether they would qualify for the loan or not.

I made a colorful hot cocoa business. I learned how to upload pictures and an about page on a website and how to make a functional calculator.

https://misdemo.temple.edu/tup12352d/hot%20cocoa%20finished/

 

Successfully built a multi-page HTML-based website for an imaginary business, complete with an interactive calculator. For this assignment, I chose to represent an imaginary custom-cut fabric shop, who sell specialty fabrics at any size needed. Customers can select their fabric of choice on our inventory page and then use our calculator to get an price quote on their selected fabric in their needed size. This project strengthened my HTML skills significantly and helped solidify my basic HTML knowledge.

Link to Site

I recently earned the AWS Academy Graduate – AWS Academy Cloud Foundations badge while studying at Temple University under the guidance of Professor Mart Doyle. This program, sponsored by AWS Academy, provided an in-depth introduction to cloud computing, emphasizing AWS services and their practical applications.

Throughout the course, I gained valuable knowledge in key areas such as AWS infrastructure, cloud security, networking, storage, and database solutions. This certification highlights my foundational understanding of cloud computing and my ability to contribute to cloud-driven projects. I look forward to utilizing these skills in future professional roles to support innovation and operational excellence.