Inspiration
Morgan and Morgan is America's Largest Injury Law Firm, and because of this, it also stands to reason that Morgan and Morgan also has America's Largest Injury case database. Case Compass seeks to leverage this database to provide value both internally and to the client.
What it does
Case Compass is a web application that was conceived with the primary objective of empowering individuals to estimate their entitled compensation in the event of an injury lawsuit settlement. Through the utilization of a complex algorithm and machine learning, this platform provides a reliable means of calculating the total amount of money that could be entitled to a user.
How we built it
This innovative platform served as a valuable learning experience, as it was developed using a range of cutting-edge technologies, including Node.js, React, and TensorFlow. Through its user-friendly interface and advanced machine learning algorithms, Case Compass provided a reliable and accurate means of calculating the total payout amount, thereby ensuring that justice is served for those who have suffered from injuries.
Challenges we ran into
As we are Students and not Lawyers at Morgan and Morgan, we (unfortunately, but understandably) do not have access to the massive, in-depth case database that we were trying to leverage. This was a significant challenge as we not only had to build and model a Machine Learning algorithm to analyze the data, but we had to build a large enough database, with data that makes some sense, to train the model.
We originally built a much wider dataset (15+ parameters, see Sebastian's branch) with a much more complex equation for the case payout so as to provide a noisier, more accurate result, but with the machines/frameworks we had, this was simply too much too soon for the initial Linear Regression model. We then instead used a much smaller set of parameters to calculate the expected payout, and this the model was able to handle with 95% accuracy.
We also created a linear regression model using Tensor Flow but we were not able to implement it into our site because of time constraints.
Accomplishments that we're proud of
Our team is proud to have created Case Compass, a web application that utilizes a complex algorithm and machine learning to empower individuals to estimate their entitled compensation in the event of an injury lawsuit settlement. Despite the challenge of not having access to Morgan and Morgan's vast injury case database, we achieved a 95% accuracy rate and successfully utilized cutting-edge technologies like Node.js, React, and TensorFlow to create a user-friendly interface and advanced machine learning algorithms. Looking forward, the platform may focus on improving the user experience, adding new features, and potentially expanding its scope to cover additional types of lawsuits beyond injury settlements.
What we learned
During this hackathon, our team gained fundamental knowledge and hands-on experience in several cutting-edge technologies, including Git, React, web design, and machine learning. For some team members, this event marked their first time delving into the world of hackathons, and we all walked away with a valuable lesson: the importance of thinking big while starting small. Our newfound skills and mindset will undoubtedly prove invaluable in future projects and endeavors, enabling us to take on even more ambitious challenges with confidence and ease.
What's next for CaseCompass
CaseCompass. However, if the platform is still actively being developed, it is possible that the project may be working on improving the user interface and experience, adding new features, and expanding its scope to cover additional types of lawsuits beyond just injury settlements. Additionally, they may be exploring ways to further enhance the accuracy of the platform's algorithm, potentially by leveraging advances in machine learning and artificial intelligence. Ultimately, the next steps for CaseCompass will depend on the project's goals and vision for the platform's future.
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