Approximate the shortest path between two points along a graph network for large sparse graphs
Using Explainable AI to show feature importance on a global level
Using a classifier to determine the failures within the system
The story of Majors, GPA's, and Salaries
Unbiased, unsupervised classification and ranking for tacos by treating all restaurants equally, giving small businesses better exposure.
Predicting equipment failures based on sensor data.
This project takes a Walmart map, traffic heat map, and various deal locations as inputs and outputs an efficient path to reach all deals
A data analysis tool for small business owners to gain insight on competitors who sell tacos/burritos
A virtual manager
We are trying to build a classifier mode to predict down hole failures based on sensor information
Predicting failures using sensor data
Multi-algorithmic approach to classifying in an imbalanced dataset
Strategic decisions for opening up a new restaurant
Wowzers! We found Waldo!
This repo holds our challenge code for the ConocoPhillips Challenge
Which area should new business target for opening tacos / burrito resturants
We tried to get as far as we could on the Restaurant data during the datathon, but ran into so many debugging problems. We're still at the debugging stage before answering questions fully.
Data science analysis of the number of vegetarian Taco and Burrito options available across the United States: Made for TAMU Datathon 2019
Our idea was to build a supervised learning model that could predict any numerical properties of chemical compounds using other known features of the compound, to prevent expensive lab activities.
Walmart prediction by a bunch of disillusioned learners.
A Datathon project
Trying to see the concentration of tacos or burritos
The Deep Money Model determines recommendations to buy, sell, or hold volatile stocks to make big gains quickly while avoiding big losses.
Built algorithms like Random Walk, BFS, and Value Iteration for solving the TSP variation
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