We help children get around Manhattan in a safer way through data driven insights.
Integrate advanced analytics into drawing basketball timeout plays
improving the selection algorithm to decrease default rate
Using headlines news, we predict the changes in stocks using Deep Learning techniques.
We used Machine Learning and Logistic Regression to predict the outcomes of NFL matches with up to 81% accuracy.
Regressional model for Bike Counts
Use deep learning and data about your apartment and surrounding area to get the best bargain for your listing!
tool generates a soccer team based on user's in-game strategies
My Submission for the CodeJam DataDive Hackathon. A Shiny App to help avoid subway traffic, built in 36 hours!
It’s the million dollar question… How much money is your place worth as a short-term rental on Airbnb?
We explored loan data to see which factors affect interest, then predicted the likelihood of defaulting/charge-off
a city can be mapped by its traffic accidents
Predicting NYC Uber demand by clusters
CitiBike Usertype Popular Path Analysis
Analysis and Prediction of Lender's Club data
A visual analysis on relationship between the Manhattan Taxi pick-up amount with subway and collision data.
Water for Power!
Counts for all forms of transportation
A Google Maps extension that enables users to find the safest route to their destination.
Predict the next item a hero will buy in DOTA2 and show it to them in real time so they don't have to enter the shop.
Datadive, Code Jam 2017- McGill University
Memoirs of deriving property prices from AirBnB data
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