AI Finance Projects for High School Students

Introduction: How can High School Students Build AI Finance Projects

What if your next high school project could predict stock prices, design a budgeting app, or even help people make smarter financial decisions? That’s exactly what students at BetterMind Lab are doing turning classroom math into AI-powered tools with real-world impact.

One of our students, Shaurya Madiraju, recently built an AI-driven stock prediction system using deep learning and an interactive dashboard. His project not only impressed mentors but also showed how high schoolers can compete with professional-grade tools.

This guide will show you how to start your own AI finance project in high school with:

• Creative project ideas

• Real-world implementations

• Datasets and resources to begin

• Lessons from Shaurya’s journey at BetterMind Lab
  

Ready to see how you can build an AI project that stands out on college applications and makes an impact in finance? Let’s dive in.

Why High School Students Should Explore AI in Finance

1. Real-World Applications

Finance touches everyone from the stock market to daily budgeting. By combining AI with finance, students gain skills that are both practical and future-proof. With machine learning, you can:

• Predict stock prices more accurately than traditional methods

• Detect fraud in transactions

• Automate trading strategies used by professionals
  

2. College and Career Readiness

Shaurya’s project is a perfect example of how a high schooler can go beyond theory. Universities and employers notice students who turn ideas into working projects, which makes these efforts a strong addition to college applications and internship portfolios.

3. Creativity and Problem Solving

Finance projects aren’t only about numbers they involve design, storytelling, and problem-solving. Shaurya combined technical modeling with an easy-to-use interface, proving that creativity matters as much as code.

Shaurya’s Project: Multi-Stock Price Prediction with Streamlit

At BetterMind Lab, Shaurya Madiraju built an AI-powered stock prediction system that combined technical depth with real-world usability.

Dataset and Foundation

He used a NYSE dataset with 700,000+ rows of stock data. To keep training efficient, he worked with the first 10,000 rows and focused on seven tickers (including American Airlines).

Instead of just closing prices, Shaurya included OHLCV data (Open, High, Low, Close, Volume), which improved prediction accuracy by up to 10%.

AI Model: LSTM Networks

Shaurya trained LSTM (Long Short-Term Memory) models, a neural network ideal for sequential time-series data.

Performance results:

• 1–7 day predictions: 85–92% accuracy

• Monthly predictions: 75–85% accuracy

• Quarterly forecasts: 65–75% accuracy
  

User Interface: Streamlit App

Shaurya didn’t stop at the model—he built a Streamlit dashboard so anyone could try it. The app lets users:

• Select different stocks

• Choose prediction ranges (1 week, 1 month, 6 months, 1 year)

• Visualize predictions with interactive charts
  

Check out a similar demo: Stock Forecasting App with Streamlit.

And here’s Shaurya’s own demo: Multi-Stock Prediction App.

Why Shaurya’s Project Stands Out

• Educational Value: Learned time-series ML, LSTMs, and UI design.

• Real-World Relevance: Similar methods are used in 85% of fintech platforms.

• Student Impact: Inspires peers to explore AI beyond the classroom.
  

How You Can Start Your Own AI Finance Project

Inspired by Shaurya? Here’s your roadmap:

Step 1: Learn the Basics

• Simplilearn guide

• GeeksforGeeks tutorial
  

Step 2: Pick a Dataset

• Kaggle Stock Dataset

• NYSE Dataset
  

Step 3: Use the Right Tools

• Python, TensorFlow, PyTorch

• Streamlit for apps
  

Step 4: Build, Test, Improve

Start with one stock → test short-term predictions → scale up.

Step 5: Add Advanced Features

• Sentiment analysis from news/Twitter

• Technical indicators (RSI, MACD, Bollinger Bands)

• Risk metrics like Sharpe ratio

  
  

  

  
  

Why BetterMind Lab Supports Student-Led AI Finance

Shaurya’s project is proof that high school students can build professional-grade AI tools with the right mentorship. At BetterMind Lab, we encourage students to:

• Tackle real-world challenges

• Explore advanced topics like neural networks

• Build apps that inspire communities
  

When students take on AI finance projects, they aren’t just learning—they’re reshaping the future of fintech.

Conclusion: Your Next Step

Shaurya Madiraju’s journey shows that high school students can push the boundaries of AI in finance by building projects. Now it’s your turn.

• Pick a project idea

• Use the datasets and tutorials provided

• Try Shaurya’s Multi-Stock Prediction App for inspiration

• Share your project with peers or submit it to BetterMind Lab for feedback
  

The future of AI finance projects isn’t limited to Wall Street—it starts with students like you who are ready to experiment, learn, and build.