Inspiration
Given the multitude of factors that influence coverage options and premiums, it can be very tedious for insurance companies to process all these claims. In an increasingly complex market, many individuals struggle to identify the best policy for their circumstances. We developed a car insurance evaluation system that streamlines this process. By assessing key factors like age, mileage, accidents, etc, our platform delivers personalized recommendations to secure the optimal coverage for their needs.
What it does
By utilizing machine learning techniques, we have created a system that predicts the appropriate policy tier, cheap, standard and expensive, based on driver data with 100% accuracy.
How I built it
Developed a grading system based on how many bad/good factors based on the clients driving record. Used prolog to define the logic. Used Ubuntu to compile and successfully test our file with an average runtime of 3.00s. Created a user interface using Java for categorizing the insurance.
Challenges I ran into
We encountered challenges in properly executing the prolog code. It was hard to ensure that all relevant factors were thoroughly considered when assigning insurance. We realized that Java required a different format for the prolog file than the command prompt.
Accomplishments that I'm proud of
We worked with sCasp for the first time and made a good product. Created something that's applicable in real life. The underlying logic for assigning an insurance type to a client is well-structured.
What I learned
Learned how to use prolog to program the logic. Integrating prolof into Java in order to build a UI.
What's next for CarInsuranceAnalytics
Finish developing our UI to be able to organize the final claim display. Add more specifications based on car price and model (luxury cars have higher insurance). Figure out how to create an interface for users to input their history instead of manually creating a specific file for each case.
Built With
- java
- prolog
- scasp