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We analyze the Neurotech@Rice track's EEG dataset from people with psychiatric disorders using the Random Forest classification model to predict diagnoses of patients who have had an EEG.
Our project utilizes historical data to forecast vehicle population. By analyzing key features such as vehicle type, fuel type, and etc, we aim to provide insights into future transportation trends.
Using multiple data sets, we investigated factors that are correlated to food deserts and food insecurity.
Hi all, I'm Edwin. I'm a civil engineer. I have recently learned the basics of python and sql, but never participated in a Hacketon before, nor did I know anything about data science.
Eamonn Keane, Alex Mirica, Mike Zhang, Gavin Firestone
We used tree-based models to analyze vehicle data from 2019 to 2023 in order to predict the vehicle population in 2024
We will use fundamental machine learning concepts, publicly available domain knowledge and the unique areas of expertise in our team to understand the data and make useful interpretations of it.
We choose Random Forest Classifier over 4 other approaches to classify different main psychiatric disorders, with optimization through choosing the top-important features for training.
Machine Learning model to predict number of vehicles in 2025
It's more than just a predictive tool—it's a sharp weapon for big data processing and precise vehicle population forecasting to take Chevron's profit to the next level.
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