🏙️ Mashhad Social Approach Study — AI-Powered Behavioral Analysis
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
