Capstone Research Project | Wright State University | April 2025

In this research project, I led a comprehensive analysis to understand the factors that influence prospective students’ decisions to pursue a Master of Business Administration (MBA). Using a rich dataset of 10,000 bachelor’s graduates from Kaggle, I explored the interplay between demographics, academic performance, work experience, financial considerations, and motivational drivers behind MBA enrollment.

Project Highlights:

Data Collection & Preparation:
Sourced a large and diverse dataset representing real-world MBA applicants. Conducted extensive data cleaning, handling missing values and outliers, encoding categorical features, and standardizing variables to ensure data quality for reliable analysis.

Descriptive & Comparative Analytics:
Generated detailed statistical summaries and visualizations to reveal patterns and trends among different candidate segments. Analyzed key variables such as age, GPA, work experience, salary, and GRE/GMAT scores to understand the profile of MBA aspirants.

Correlation & Feature Analysis:
Explored relationships between career aspirations, financial readiness, and academic achievement. Identified significant correlations that impact the decision to pursue an MBA, highlighting the importance of work experience and financial planning.

Predictive Modeling & Machine Learning:
Developed and fine-tuned multiple classification models—including Logistic Regression, Random Forest, and XGBoost—to predict the likelihood of MBA enrollment. Assessed model performance using accuracy, precision, recall, and ROC-AUC metrics. Performed feature importance analysis to surface the top factors influencing candidate decisions.

Cluster Analysis & Market Segmentation:
Applied K-means clustering to segment candidates into groups such as career-driven professionals, financially constrained aspirants, and early-career seekers. These insights enabled the design of personalized marketing and recruitment strategies for business schools.

Strategic Marketing Recommendations:
Converted analytical findings into actionable marketing strategies for higher education institutions. Proposed targeted campaigns emphasizing career advancement, financial support, and program flexibility, as well as personalized messaging informed by predictive analytics and segmentation.

Executive Reporting & Data Visualization:
Created compelling visualizations and executive summaries to communicate complex findings to non-technical stakeholders. Presented a clear narrative linking data insights to marketing outcomes.

Impact:
This project demonstrated how advanced analytics and machine learning can transform student recruitment in higher education. By uncovering what motivates and challenges prospective MBA students, my recommendations empower business schools to develop data-driven, student-centric marketing initiatives—improving engagement, application rates, and program alignment with candidate needs.

Skills & Tools Used
Data Preparation (Pandas, NumPy)

Machine Learning (Scikit-learn, XGBoost)

Data Visualization (Matplotlib, Seaborn)

Statistical Analysis, Correlation & Feature Engineering

Predictive Modeling, Classification, Clustering

Market Segmentation & Strategy Development

Executive Reporting & Presentation