How our Mentored AI Project Helped Eeshan Stand Out in College Applications

Introduction: AI Projects Helped Stand Out in College Applications

Can a high school student build an AI that studies markets the way analysts do? That question launched this project.

This is the story of Eeshan Ajmera, who set out to design a Stock Market Predictor a learning journey that brought together finance, data patterns, and machine intelligence.

About Eeshan

Eeshan has always been curious about why markets move the way they do. His interest wasn’t driven by trading hype, but by the deeper question of whether financial patterns can truly be understood. With a growing interest in computer engineering, he wanted to test whether technical skill could help him decode something as unpredictable as the stock market.

He wasn’t looking for shortcuts or guaranteed predictions. He wanted to explore how analysts use data, what signals matter, and how AI can help identify trends. The motivation was simple: understand the discipline behind market analysis, not the thrill of it.

About the Project

Predicting stock movement is one of the hardest problems in finance. Markets reflect human behavior, global events, and complex signals. But machine learning can still uncover patterns that the human eye misses.

Eeshan’s Stock Market Predictor analyzes historical market data, calculates indicators, and uses those patterns to estimate whether a stock is likely to rise or fall. The model doesn’t claim certainty it provides structured reasoning in a domain defined by uncertainty.

Explore How AI in Finance is a Great Passion Project Idea

How the Student Built It

Eeshan worked through a structured mentorship flow that emphasized reasoning before modeling. He began by learning how technical indicators work, why traders use moving averages, and what makes financial data noisy. Only after understanding the logic did he start building.

The dataset challenged him. Trends were inconsistent, outliers distorted results, and early models overfit quickly. His turning point came when he learned how to smooth noisy signals and validate models with discipline instead of intuition. That shift from guessing to testing was the real breakthrough.

He strengthened skills that matter for both finance and engineering: analytical thinking, data preprocessing, responsible experimentation, and iterative improvement.

Student Impact & Outcomes

This project gave Eeshan more than a predictor it gave him a framework for thinking about complex systems. He learned how professionals analyze markets, why predictions must be approached carefully, and how AI supports decision-making rather than replacing it.

For future university applications, Stock Market Predictor becomes a compelling portfolio piece. It shows maturity, statistical reasoning, technical experimentation, and a realistic approach to uncertainty. These qualities stand out in competitive engineering and finance pathways.

He now has hands-on experience that will help him pursue deeper coursework, future internships, and advanced projects in data-driven decision systems.

Eeshan’s View on BetterMind Labs

“BetterMind Labs was a space where I learned real, applicable skills and worked closely with others who genuinely cared. It’s not a surface-level experience; it’s one where collaboration felt natural, the mentorship was thoughtful, and the friendships I built were real and lasting. I appreciated the in-depth guidance, the supportive team, and the way every task felt like it had purpose.” — Eeshan

FAQ

1. Why is stock prediction a strong AI project for students?

It teaches how data, uncertainty, and real-world variables interact. Colleges value projects that blend technical skill with financial reasoning.

2. How does mentorship improve a project like this?

Mentorship helps students move beyond trial-and-error. They learn how analysts structure problems, validate models, and avoid common pitfalls.

3. Does this project strengthen college applications?

Yes. It highlights data literacy, engineering mindset, and the ability to build complex, meaningful projects traits highly valued in selective programs.

4. What skills does a student learn from building a stock predictor?

They gain experience with data preprocessing, signal analysis, evaluation metrics, and responsible AI skills that translate directly to advanced coursework.

Conclusion

Eeshan’s journey shows what real AI learning looks like: careful thinking, structured experimentation, and a project rooted in real financial challenges. This is the kind of work that builds confidence and strong academic narratives.

Explore more student journeys at bettermindlabs.org.