Projects
Predictive Modeling on the Titanic Dataset
Overview
Objective: To predict passenger survival on the Titanic using machine learning algorithms.
Dataset: The Titanic dataset, retrieved from Kaggle, contains information about passengers, including features like age, class, and fare.
Approach: Employed exploratory data analysis (EDA), decision tree modeling (rpart), and Naive-Bayes Classification.
Methodology
Data Exploration & Preprocessing:
- Conducted thorough EDA using plots and tables to understand data distribution, missing values, and relationships.
- Addressed missing values through imputation or removal.
- Ensured data quality and consistency.
Decision Tree Algorithm (rpart):
- Applied the rpart algorithm to predict passenger survival.
- Decision trees recursively split data based on features, creating a tree structure.
- Evaluated model performance using a confusion matrix
Naive-Bayes Classification:
- Implemented Naive-Bayes Classification, assuming feature independence.
- Evaluated model performance using a confusion matrix.
Model Comparison:
- Compared accuracy between the rpart model and Naive-Bayes.
- Both models exhibited similar accuracy.
- The 95% confidence interval confirmed their comparable performance.
Conclusion
The decision tree and Naive-Bayes models provide reliable predictions for passenger survival.
These results demonstrate proficiency in data analysis, modeling, and evaluation.
Experience
Rutgers Innovation, Design & Entrepreneurship Academy
Design Researcher: Energy & Environmental Innovations
May 2024 - August 2024
-
Collaborated with the Office of Community Outreach at St. Peter’s University Hospital, analyzing business practices and contributing insights to address food insecurity. Also researched K-8 educational efforts on food insecurity awareness.
Rutgers Global
International Student Orientation Leader (OL)
Summer 2024 - Fall 2024
-
Collaborated with the OL team to create an informational guide for new students and their families, serving as a role model and support system for incoming international students. Provided information on academic, personal, and social resources at Rutgers New Brunswick. Also worked with faculty and staff to ensure a welcoming experience for international students.
Rutgers University Student Assembly (RUSA)
RUSA Allocations Board Treasurer
Fall 2024 - present
-
Data Engineering & modeling techniques to analyze budget request data & determine a fair allocation of available funds (over a million dollars’ worth of program funding) to different organizations at Rutgers University New Brunswick campus.
- Microsoft Excel power queries to compile funding reports.
Rutgers Women in Computer Science
Data Analyst Research Intern: Computer Science Education
Spring 2024 - present
-
Design and conduct surveys for the Rutgers Computer Science & ITI community, analyze responses using Excel power queries and R code to identify differences across majors, and explore how women’s experiences impact academic major decisions.
Certifications
J.P. Morgan Software Engineering Virtual Experience on Forage
June 2024
- Set up a local dev environment by downloading the necessary files, tools, and dependencies.
- Fixed broken files in the repository to make web application output correctly.
- Used JPMorgan Chase’s open-source library called Perspective to generate a live graph that displays a data feed in a clear and visually appealing way for traders to monitor.
Python for Data Engineering
June 2024
SQL Essential Training
May 2024
HTML Essential Training
May 2024
CSS Essential Training
May 2024
About
Hello! My name is Chinyere Ugwuanyi. I am studying CS at Rutgers university-NB and I have an interest in Data Analytics, Web Design & Development, and Software Engineering. I am very enthusiastic about getting into hands-on learning opportunities and projects. Please feel free to reach out/connect with me! This is my email: chinyere.ugwuanyie@gmail.com
Contact Me