About the Project

I created this project because I’ve seen firsthand how academic stress and mental health challenges can affect students. I wanted to use data to tell a story—a story that could help educators and mental health professionals spot early warning signs and intervene before things get worse.

What Inspired Me

My own experiences during school, coupled with conversations with friends and mentors, made me realize that there’s a lot of untapped potential in using data for mental health. I was driven by a desire to make a difference in an area that is often overlooked.

What I Learned

Working on this project taught me the value of rigorous data preprocessing and the art of feature engineering. I learned how small adjustments in the model—like tuning hyperparameters and optimizing thresholds—can lead to big improvements. Most of all, it showed me how powerful data can be when it comes to addressing real-world problems.

How I Built It

I started with raw data, cleaning and transforming it to handle missing values and create meaningful features. From there, I applied statistical tests to identify key relationships and built several machine learning models. Experimenting with PCA and hyperparameter tuning allowed me to refine these models until they performed well in predicting mental health outcomes.

Challenges Faced

The journey wasn’t without hurdles. Managing incomplete data and ensuring my models didn’t overfit were constant challenges. Balancing complexity with clarity was a learning curve, but every challenge pushed me to improve my skills and approach. Despite the setbacks, each obstacle was a step toward creating a more robust and insightful project.

Built With

• fastapi
• nextjs
• python