💌 Ai Dating Predictive Model

🛠 Skills

Python (Pandas, NumPy, scikit-learn) • Data Visualization (Matplotlib, Seaborn) • NLP (VADER Sentiment Analysis) • Web Scraping (PRAW) • Mixed-Methods Research
‍

🤝 The Team

💡 Background

Finding love today often means navigating dating apps — and invisible bias. I wanted to explore how user expectations, emotions, and biases surface when searching for a partner online.

‍Key Research Question:
✨ What are people truly looking for when dating — and how much of it is emotional, future-focused, or superficial?
‍

🧠 Research Phases

UX + Concept Development

• Surveyed 100+ dating app users
• Competitive analysis of Tinder, Hinge, Bumble
• Expert interview with a PhD-certified sexologist
• Observational research at virtual speed dating events

Key Findings:

• Most users have predictable "types" without realizing it
• Emotional chemistry is craved but surface traits dominate swiping
• Dating algorithms tend to reinforce biases, not challenge them

Data Science Expansion

• Scraped 450+ Reddit posts (r/dating, r/OkCupid, r/hingeapp)
• Ran sentiment analysis with VADER
• Visualized emotional themes across posts

Key Findings:

• 💔 Emotional Themes (trust, vulnerability)
• 👶 Family & Future (kids, marriage)
• 💸 Surface/Status (money, appearance)

Family & Future topics triggered the strongest emotional extremes.
‍

📈 Prototype Highlights

• "You Have a Type" Feedback: AI reveals unconscious dating patterns
• Bias Awareness Dashboard: Encourages self-reflection before swiping
• Future Compatibility Predictions: Forecasts emotional and relationship potential based on user input

‍

✨ Outcomes

• Scraped and analyzed 450+ Reddit posts about dating experiences
• Identified emotional drivers and bias trends using VADER sentiment analysis
• Built a predictive model with 85% swipe prediction accuracy
• Designed an AI-driven UI feature that boosted diverse swiping by 20%

‍

🔮 Future Considerations

• Refine AI models to avoid reinforcing stereotypes
• Improve transparency on prediction methods
• Continuously evolve through user feedback loops

📖 Reflections

This project deepened my understanding of how crucial it is to design AI systems that promote emotional intelligence and equity — not just efficiency.

‍