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An analysis of frequencies of key words in computer science practice problems
TAMU Dathathon (October 19 - October 20)
Classification Model for ConocoPhillips Competition
Using a deep learning model to perform a linear regression to predict equipment failures.
Have you ever wanted a specific dish at a restaurant, but don't know which has it? With this web app, specify a dish (and ingredients) you want, and it will give you restaurants nearby, with key data.
Have you ever wanted to start a taco or burrito shop, but didn't know how to be competitive? That's where Taco 'bout It comes in.
After several testing of algorithms we found the best one! We are able to predict correctly the target values with the given data.
Solution to ConocoPhillips challenge using neural networks
Comparing proportion of Hispanic populations and available burrito/taco options
The project was to predict oil and gas equipment failure based on sensor data collected from the equipment
Do you know where to find your favorite tacos?
A cross-platform app for data visualization of equipment failure prediction
Modified Breadth First Search to minimise search time for optimal path
where are the best places for taco/burritos lovers? does Texas love taco more than burritos? come to learn the fun facts with us using data analysis
Predict equipment failure from data using classical machine learning approach
We predict equipment failures using a simple model accurately and maintain interpretability of our results
Regression model that accurately recommends a group of users which restaurant to eat at by incorporating their preferences and highly rated items on Yelp
Visualizing the accidents in Austin
Texas A&M Datathon
A binary classifier that accounts for class imbalance to predict equipment failures given data from 107 sensors.
This .tech domain can be used for all things to do with computer science.
Tacos and Burrito Restaurants, Mapped
ConocoPhillips Tamu Datathon contest
Neural networks are notoriously overconfident in their predictions. We use architecture search to find a distribution of architectures and use it to construct a Bayesian ensemble for outlier detection
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![eatsy [Placed 3rd, TAMU Datathon]](https://d112y698adiu2z.cloudfront.net/photos/production/software_thumbnail_photos/000/867/031/datas/medium.png)
