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Convolutional Graph Neural Network for classifying patients with mental disorders using EEG signals.
Analyzing and finding solutions about food deserts using anayltical processes and visual models/maps.
#Chevron #Vehicle Population Prediction #Machine Learning
Envisioning a world gone green with predicting vehicle population using feature engineering and statistical analysis.
Driving the Future: LightGBM Vehicle Population Forecasting for Smarter Sustainability
AI tool to transform mental health diagnostics and address global mental health challenges.
Mental health diagnosis is subjective and tough. At the 2025 Rice Datathon, we built ML models using EEG data to diagnose people . Our work shows AI’s potential to impact mental health diagnostics!
Over 17 million Americans live in food deserts, where accessing fresh and healthy food is a challenge. In this analysis, we explore key factors affecting food accessibility and recommend policies.
Mapping Food Access, One Tract at a Time.
Movie reviews are essential in picking what to watch next. However, they often contain spoilers, ruining the viewing experience. We ask: is it possible to determine whether a review contains spoilers?
We decided to incorporate sustainability data into our chevron dataset and host an AI agent.
Automating Machine Learning Model Selection and Optimization with FLAML for Enhanced Data Insights on the Chevron Dataset
Predicting Vehicle Population for 2025
A serverless deployed trained model for Chevron 2025 vehicle prediction
We are rather inexperienced regarding programming, I am a first-year Computer Science student, and 2 more of my teammates are pursuing degrees in Business, but oh no! We had GREAT FUN!
Predicting Vehicle Population using Non-Linear Machine Learning Algorithms
We are nomad hackers and we get things right hacking. let it be a hackathon or a Datathon.
Derived and Added Features in the Dataset !!
We analyze NeuroTech@Rice Data to discover Neurological Disorders using complex Machine Learning models.
Empowering Mental Health Insights Through Data-Driven Innovation.
Decoding Minds: Advancing Mental Health Diagnostics with EEG and AI
We implement a vehicle population forecasting method using an ensemble of basis CatBoost models with Bayesian hyperparameter tuning.
Using data from the USDA's Food Research Atlas, our team identified key indicators that highlight the most vulnerable areas. Programs like SNAP should focus more on these high-risk areas.
This is our submission for the 2025 Rice Hackathon for the Neurotech track! We present a machine learning model capable of predicting certain psychiatric disorders given EEG data.
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