RACING

Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for RACING

Our team was inspired to create Racing Insights Hub after observing how racing industries depend on data-driven decisions, which motivated us to build a system that predicts pit-stop lap time, driver score, and tyre condition from key racing inputs. Throughout the project, we learned how machine learning models map input features like speed, lap time, brake pressure, tyre condition, driver behaviour, and throttle into meaningful outputs using the function . We collaboratively developed the frontend using HTML, CSS, JavaScript, and Bootstrap, while the backend was built in Python using Scikit-Learn to train and deploy our prediction model. As a team, we faced challenges such as cleaning and aligning the dataset, selecting the best ML algorithm, and ensuring smooth communication between the JavaScript frontend and Python backend, but overcoming these obstacles helped us understand real-world development, teamwork, and the end-to-end pipeline of building a complete predictive system.

Built With

• bootstrap
• css
• html
• javascript
• scikit-learn
• uvicorn