🎓 Predicting Full-Ride Scholarships: A Data-Driven Analysis of College Admissions

Mon, 20 Jul 2026 00:00:00 +0000

Executive Summary

Analyzed a synthetic dataset of 1 million college applicants to identify the distinguishing characteristics of students who secure admission versus those who don’t, with special emphasis on modeling full-ride scholarship eligibility. The project simulates both domestic and international applicant profiles, providing actionable insights for students aiming for competitive financial aid packages.

Problem Statement

With college costs rising and acceptance rates plummeting at top institutions, understanding what truly drives admission decisions — and scholarship awards — has never been more critical. This project answers:

What minimum qualifications do students need for admission?
What separates full-scholarship recipients from merely admitted students?
How do international students fare in this competitive landscape?

Dataset

Property	Value
Source	Kaggle (Synthetic Gen Z College Admission Dataset)
Size	1,000,000 rows × 20 features
Target Variable	`admission_status` (0/1)
Class Distribution	88.1% admitted, 11.9% rejected
Missing Values	Zero

Features: GPA, SAT/ACT scores, essay & interview scores, recommendation ratings, AP courses, extracurriculars, leadership positions, volunteer hours, coding projects, online certifications, family income, demographics.

Methodology

1. Exploratory Data Analysis

Distribution analysis of all numeric features
Correlation matrix to identify multicollinearity (notably SAT vs. ACT)
Demographic breakdowns by gender, state, and income brackets

2. Scholarship Score Engineering

Developed a weighted composite Z-score incorporating 13 features:

Feature	Weight
High School GPA	15%
Essay Score	13%
SAT Score	12%
Interview Score	12%
ACT Score	10%
Recommendation Score	10%
Leadership Positions	7%
Extracurriculars	6%
Volunteer Hours	5%

3. International Student Modeling

Simulated international profiles by:

Boosting SAT/ACT scores (+8%/+6%) and essay quality (+5%)
Reducing AP course access (-30%) and family income (-40%)
Adding English proficiency (IELTS equivalent) and visa type features
Applying adjusted weights emphasizing essays and diverse activities

4. Threshold Analysis

Full scholarship requirements (99th percentile):

Full Scholarship (99th percentile)
├── SAT: ≥ 1,450
├── GPA: ≥ 3.80
├── Essay: ≥ 92
├── Interview: ≥ 91
├── Leadership: 3+ positions
└── Volunteer: 200+ hours

Key Insights

For All Students:

Holistic excellence matters more than perfection in one area — top 1% are in the 90th+ percentile across ALL criteria
SAT/ACT scores show diminishing returns above 1500/34 — essay and interview scores become the true differentiators
Leadership positions have 2.3× the impact of general extracurricular participation

For International Students:

Scholarship odds are 3–5× lower than domestic students
Essay quality becomes 80% more important (weighted at 18% vs. 12%)
AP course disadvantages can be offset by 3+ online certifications and 3+ independent coding projects

Statistical Correlations with Scholarship Score

Feature	Correlation
SAT Score	+0.72
High School GPA	+0.68
Essay Score	+0.65
Interview Score	+0.61
Leadership Positions	+0.52
AP Courses	+0.38

Visualizations

Scholarship score distribution (KDE) comparing international vs. domestic
Radar chart showing profile differences
Feature importance bar chart
Scatter plots: GPA vs. SAT with scholarship tier coloring
Financial need impact analysis
Multi-level scholarship threshold visualization

🛠️ Tools & Technologies

Data Processing: Pandas, NumPy
Statistical Analysis: SciPy, Z-score normalization, percentile ranking
Visualization: Matplotlib, Seaborn (KDE, radar charts, heatmaps)
Environment: Google Colab, Jupyter Notebook
Version Control: Git / GitHub

Business & Educational Impact

Stakeholder	Value
University Admissions Offices	Data-driven benchmarks for merit scholarship allocation
Prospective Students	Clear, quantifiable targets for competitive applications
College Counselors	Evidence-based advising strategies
EdTech Platforms	Feature importance for building recommendation engines

Future Improvements

Implement ML models (Random Forest, XGBoost) for prediction
Add fairness/ethics analysis (gender bias, income bias)
Create an interactive Streamlit dashboard
Incorporate real-world data from Common Data Set
Build a recommendation engine suggesting improvement areas

🏙️ Mashhad Social Approach Study — AI-Powered Behavioral Analysis

Mon, 20 Jul 2026 00:00:00 +0000

Overview

A data-driven computational study investigating the gap between romantic interest and actual approach behavior among young adults in Mashhad, Iran, a city with a unique blend of religious tradition and modern student culture. Using a self-collected dataset of 1,000 participants, this project applies machine learning and statistical modeling to uncover hidden behavioral patterns, paradoxes, and gender asymmetries in human social initiation.

🔍 Focus

The study examines how fear of rejection, self-perceived attractiveness, religiosity, daily habits, and digital behaviour relate to whether young adults actually approach someone they are interested in, and how these patterns differ by gender.

🧪 Methodology

Dataset: 1,000 participants (balanced gender distribution), 30-item structured questionnaire
Data Cleaning: Pandas, NumPy — Persian text-to-numeric encoding pipeline
Statistical Testing: SciPy — Independent T-Tests across 29 features with Bootstrap confidence intervals
Machine Learning: Scikit-learn — Random Forest, Gradient Boosting, Logistic Regression, K-Means clustering, Isolation Forest, t-SNE
Deep Learning: PyTorch — 4-layer neural network classifier + Autoencoder for anomaly detection
Explainability: SHAP — Feature importance analysis revealing that Social Sacrifice Index (willingness to risk social prestige) is the strongest predictor of approach behavior
Visualization: Matplotlib, Seaborn — 12 publication-quality figures

📊 Results & Impact

Built a conceptual model mapping psychological, economic, and spatial factors to social action
Generated 12 high-resolution figures suitable for academic publication

🛠️ Technologies Used

Python Pandas NumPy SciPy Scikit-learn PyTorch SHAP Matplotlib Seaborn Google Colab

💡 Personal Contributions

Designed and executed the complete end-to-end data science pipeline
Engineered 200+ features including pairwise interactions and polynomial transformations
Implemented 3 anomaly detection methods (Isolation Forest, LOF, Autoencoder) with consensus voting
Applied fairness metrics to ensure the model is not gender-biased
Created an automated interpretation engine producing 100+ analytical insights

Keywords: Computational Social Science Human-Computer Interaction Behavioral Machine Learning Gender Analysis Socio-Religious Context Anomaly Detection Explainable AI

Scikit-Learn | Abbas Hamidavi | عباس حمیداوی | AI Researcher