The System will allow users to book a hotel ticket using voice commands.

1. The system will ask travel details to the user like origin, destination, travel date, number of guests, etc.
2. The user will answer the queries and based on this he will get hotel suggestions(on other channel also like email/whatsapp).
3. The user will select a hotel and further enter his contact details like phone number, email address, etc.
4. The user will receive a payment link on his email address. (You may provide a dummy link of upi payment)
5. After successful payment he will be informed about the booking status whether it is booked or failed.

Assumption

• APIs required to book a flight would be available (like apis for search hotel, fetch hotels results, book hotel ).
• Assume that the user will complete the payment in 30 seconds.

Use manuals available for one of the products at docs.accops.com and build a ChatGPT enabled chatbot that can answer the questions for a user of the manuals at docs.accops.com

Do it at least for one of the products listed on docs.accops.com

Cardiovascular disease (CVD) is a major cause of morbidity and mortality worldwide. Early detection of CVD can help in timely treatment and management. Medical imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) have shown promising results in detecting CVD.

Your task is to build a predictive model that can accurately predict the risk of CVD based on medical imaging data. You are given a dataset containing medical imaging data (CT/MRI scans) of patients along with their demographic and clinical data such as age, gender, blood pressure, cholesterol levels, smoking status, etc. The dataset also contains information about whether each patient has CVD or not.

Your goal is to build a predictive model that can accurately classify whether a patient has CVD or not based on their medical imaging data and other clinical variables. The model should be evaluated on a holdout dataset using appropriate metrics such as accuracy, precision, recall, and F1 score.

To successfully complete this hackathon, you need to:

1. Perform exploratory data analysis to gain insights into the data and identify any patterns or relationships.
2. Explore the medical imaging data and perform any necessary data pre-processing.
3. Build a predictive model using appropriate machine learning algorithms that can predict the risk of CVD based on medical imaging data and other clinical variables.
4. Evaluate the performance of your model using appropriate metrics.
5. Submit your model and code along with a brief report explaining your approach and results.

Note: Make sure you adhere to the Health Insurance Portability and Accountability Act (HIPAA) regulations while working on this dataset. Ensure that you do not disclose any personal information about the patients while working with the dataset.

5ireLabs will be giving away cash prizes of INR 10,000 for the best hacks built on 5ire during our community hackathons.
Hackathon teams that want to keep working on their projects after the hackathon can apply for grants from 5ireLabs up to $25,000 USD! 💵

The cash prize of INR 10,000 and a swag-kit (if available) will be awarded to:
- The best-beginner level projects just starting out to build on 5ire.
- The best-intermediate level projects that go into depth, demonstrating higher-order code.
- The best-advanced level projects that are almost ready for full-time development.

Track and Participation Details