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Integrating diverse data can provide a comprehensive multi-layer view of a biological system
A Cloud-based Application on Azure Platform in the Utility Industry
Evaluate QB performance compared to their salary.
We analyzed the different countries' censorship of internet addresses and looked for patterns and inconsistencies.
Helping students.
Studying computer science in college is hard, and I get it. So, I present Nerdvana, the ultimate tool to help you navigate your CS journey.
Your Perfect Brew: Discover Tailored Coffee Spots Just for You
Most voter data files are hidden behind obscure archives and private companies hoping to profit off smaller political orgs. We've built a page to access specific voter info, see maps, and benefit all.
GovPro lets users analyze government purchases from companies over time, check spending by categories, and map food purchases, finding opportunities to consolidate for enhanced efficiency and savings.
Our app ‘Floodlives Matter” leverages AI and machine learning to predict real-time flood intensities and benefits undeserved communities without access to such data.
A memory-based game with visual feedback on player's score.
Petal Pals is on a mission to transform under-resourced communities disproportionately affected by climate change. Through data-driven technology, we track environmental issues in urban green spaces.
What if there were a model that reviewed course selection by prof difficulty and class trend to predict a students predicted GPA? McGPA does this by training on McGill data for McGill students.
What's the song you go to when you're feeling nostalgic? How did they change as time goes on?
Should we wish to go back to the past if the past wasn't good enough? Our project will explore diversity patterns from 2000 and 2010 within Metropolitan and Micropolitan America.
We created visualizations to show that larger states with sparse areas are more prone to wildfires. Most of the fires are in Western states.
We used predictive modeling to create a model that would predict the severity of one's diabetes, estimated based on the number of times they have been diagnosed.
In our analysis of patient readmission data, we employed two supervised machine learning algorithms: Logistic Linear Model (GLM) and Decision Trees.
Changes and trends in popular music have heavily shaped the lives of each generation. We aim to find some of these trends and determine what contributes to creating a hit song in the modern day.
Looking for correlations between the National Walkability Index and the change in population in the US from 2000-2010
Healthcare Track: This project aims to analyze what factors can be used to predict whether or not a diabetes patient is readmitted to the hospital after a medical interaction.
Creating Better Healthcare Outcomes: Analyzing Data to Predict Patient Hospital Stay Durations, and Driving Informed Decisions for Improved Patient Care.
Machine Learning, Song Hit Prediction, SVM, Random Forest, XGBoost, Logistic Regression, Spotify
This project utilizes Spotify Web API to extract data from songs between 2000-2010 and conducts a series of time series, genre, and mood analysis
