HackTable was a global online hackathon that took place from Saturday, November 28 to Sunday, November 29. Students of all levels of computer science experience ranging from novices to experienced coders could find categories that would challenge them to think critically.
This hackathon was sponsored by EazyML; with a vision to provide all students the opportunity to pursue STEM pathways and connect with like-minded people from all over the world. EazyML provided their platform and ideas for the hackathon. Everybody had the opportunity to use EazyML’s platform to either predict and mitigate the spread of California’s wildfires with the date provided or predict whatever they wanted to using their own dataset.
There were many projects that used EazyML’s machine learning to predict real world consequences. Based on the complexity and the use of EazyML within the application, the company was able to award prizes to the top 3 projects which best represented applications of the platform. These are a selection of the best projects submitted:
Similar to the previous hackathon that EazyML hosted, many participants used EazyML to analyze the California wildfires. Paying attention to nature and how it is being affected is very important, especially during a time in which people are not outside as much.
Being able to predict where and how severe fires will start can be an amazing first step in mitigating the spread of these dangerous fires. One project - Extinguish - showcased the use of EazyML to do exactly that.
By integrating EazyML’s analytics with the data on California wildfires, this team was able to create a website that warned users about which areas would be most affected. Users can see the history of how wildfires have spread throughout California on a map that displayed where each fire was located and the relative severity of each fire. Recent news concerning wildfires is also highlighted, allowing the user to stay up to date on any fires that have occurred since. Finally, a graph of California is displayed with an overlay of a prediction made by EazyML of areas that would have the most acreage burned by the fires. This will help the people in California figure out how much their area will be affected and what to do about the current wildfires.
Another project which was especially relevant is the Covid-19 Simulator. Even though a vaccine has been researched and shown to work, the US is still experiencing its 3rd wave of Covid-19. With the vaccine not being expected to be fully released to the public for many months, the US is still greatly in need of projections of where the disease will spread so that the vaccine can be distributed accordingly.
The Covid-19 Simulator takes in the most current data on Covid cases by county in the US, using factors like population, location, and each county’s response to things like mask mandates, restaurant restrictions, gathering size limitations, and others. These predictors are combined with the number of cases in each county, and run through EazyML. Then, by taking what each county may do in the future and putting in factors corresponding to loosening or tightening of restrictions, a future case amount can be predicted for that county. By running this program for a user’s own county, they are able to see how much better or worse their area might get in regards to coronavirus cases. The state and federal governments may also be aided by such predictions to allocate funds or other materials to certain areas that may be affected worse than others in the future.
The third project which was recognized by EazyML addresses motor vehicle accidents: Safe Steer.
Safe Steer is an application which can predict the safety of a road and show where wcrashes have occured. By taking in real-time data from accident reports and considering factors such as traffic, weather conditions, and condition of the road, Safe Steer is able to tell someone looking to drive on a certain road whether it is safe or not. By running the aforementioned factors through EazyML, it is able to give a rating of each road out of 3. If a trip is being planned out which needs to pass through a road with a low road rating as predicted by EazyML, a modification can be made beforehand so the route will be as safe as possible. In addition, the application is able to predict accident hotspots on the road, so if the road needs to be taken certain sections of the road may be able to be avoided.
Along with the three projects that won awards, many other submissions were able to use EazyML in their application. One such project is able to analyze job postings to tell if the posting is real or fraudulent, to help people not waste their time dealing with job advertisements that are just trying to collect data. Another project was able to use EazyML to take generated code and predict whether or not the code will be scalable. Other projects that addressed either the California wildfires or the Covid-19 crisis were also created, which both took a different angle to each topic than the projects described above.
There is an abundance of data that anyone can collect from the world, and the uses for any of this data is infinite. By using EazyML, you can manage data and make predictions of anything around you. Whether you are tackling a large problem such as the California wildfires or Covid-19, or something smaller, the EazyML platform and API can give you the tools to predict your world.
You can get started with EazyML on https://eazyml.com or https://eazyml.com/docs as a developer for building on the API. If you have more ideas or want to reach us for Hackathon sponsorships, please reach us at info@eazyml.com