## Inspiration

You're in your kitchen and want to make food. You have a limited set of ingredients and wonder what you can cook!
This is where Recipe Express comes in.

## What it does

Recipe Express allows you to enter your pantry items and look up the possible recipes you can make with what you have. In case you don't have all the items, you can place an order for the missing items through the platform and those show up at your doorstep. This allows you to save time and be able to cook your favorite meals whenever you want.

## How we built it

We built the front end of this application using React. The back end was made with MongoDB and Python-Flask. The work was divided into groups of two. David and Arnab Did front-end, while Matt and Nick did Back-end

## Challenges we ran into

One challenge was that the data recipe data we had access to was not clean. There was a lot of irregular extra text around the ingredients (i.e. "1/2 cup of broccoli"). We overcame this by using regular expressions in our database queries to match to the terms of interest.

Another challenge was combining the back end functionality with the front-end. We had never touched these languages/frameworks before this weekend.

## Accomplishments that we're proud of

## What we learned

## What's next for Recipe Express

An appearance on Dragon's den. We're confident Recipe Express has the potential for big investments. There are a lot of ways Recipe Express can grow and be developed. Some key services to add are automated grocery ordering, ingredient auto-complete (for ease of use), and if the user base expands employing ML algorithms to provide recommendation service based on user-history and recipe use correlation (even before this we could scrape data from the web on reviews for recipes and use that as a pseudo-metric for ranking recipes and deciding on the order to present them to a user after a search).
# Inspiration

Oftentimes, getting into investing and trading can be a nightmare. It's often overlooked - sometimes because of negative stigma, but also because of the harrowing complexity of diving into it. As a result, we wanted to create an easy to use tool for beginners to use in order to learn more about market basics as well as provide them resources to get started.

# The Solution - Nashaq: A Nasdaq Bot

We created a chatbot specialized for Facebook Messenger to aid those interested in investing and trading.

The bot provide easy financial decisions and data visualization, and includes important indicators such as P/E ratio, Beta, One-Year Target, and 50-day moving average. Allows you to easily send results to friends and presents a key summary page with a financial recommendation.

# Challenges

Integrating with Wit.ai, making sure the NLP was trained properly and was accurate. Ultimately, chose to use JSON objects to check against user input since return results were more specific and precise. Using multiple npm packages and making sure they all transferred functionality over was also tricky. Initially written in python, but then switched to node.js to streamline everything.

# Goals

Implement a better AI/NLP model to interpret the data, perhaps with Luis AI on the Azure platform.

# Accomplishments/What we learned

We're proud of creating a full stack messenger bot that will allow users to easily find financial data for companies they are interested in learning more about. First time creating a bot, and got more familiar with node.js and using multiple modules.
## Inspiration

Customer acquisition costs are high for credit cards. So high, that banks will even give out new iPads for anyone who signs up for their credit cards. On our flight from Montréal to Boston, we saw credit card ads plastered everywhere, offering sky-high sign-on bonuses. While banks are eager to reduce their customer acquisition costs, customers are bogged down by confusing terms and conditions, convoluted points programs, and choice fatigue.

With enough knowledge, customers can use the strategy of credit churning - obtaining multiple credit cards to capitalize on high sign-on bonuses and rewards. However, identifying the right credit card for oneself is usually a tedious process, and the card that a user selects might not fulfill all their specific needs.

## What it does

CardMaster simplifies the credit card churning process, offering our users personalized credit card recommendations based on their demographics (occupation, income, credit score, spending habits and travel preferences), saving the time and effort required for extensive research. Apart from letting users reap the rewards, it also immerses them through mixed-reality experiences showcasing a view of the specific benefits cards’ offers would bring them. Put on your AR/VR headset and, with a few swipes, our "cardmasters" are virtually transported to the destinations you've been dreaming of visiting. They can virtually explore luxury resorts, see the world from your favorite airline's first-class cabin, and experience the perks of each recommended credit card firsthand.

## How we built it

We built the mixed-reality front-end with AR/VR using the Apple XCode-Beta development kit, using Swift, SwiftUI, RealityKit, Alamofire, concurrent MVVM design architecture, and of course, VisionOS. We created a microservice backend, using Python, Flask, Bitarray, Scikit-learn, NumPy, Pandas, Google Cloud Platform (GCP), Cohere, and Docker. As a bonus, we built a demo website to showcase CardMaster, using JavaScript, React, and Tailwind at <https://cardmaster-hackharvard.netlify.app/>. Prior to developing the interface, we designed mockups using Figma, keeping simplicity, ease-of-use, and aesthetics in mind to create a seamless user experience.
For added functionality, we also integrated an AI financial advisor which we prompt engineered for Cohere’s large language model.

The recommendation algorithm was accomplished using a Jaccard Similarity Index to find the similarities between users based on their demographics, such as credit range, annual budget, income, profession, and travel frequency. This was then mapped to the credit card tastes of each user. Linear regression was then applied to these results to determine whether or not each card was deemed a suitable fit.

## Challenges we ran into

The VisionOS frontend environment was particularly challenging to work with due to the difference in the base components for VisionOS compared to conventional Swift components, which posed an interesting challenge for our UI/UX design. Additionally, navigating the VisionOS environment was also limited due to the lack of resources available online, as the Apple Vision Pro has not yet been released. We encountered difficulties in accessing support for many features we wish to incorporate.

One of the more difficult issues we faced was designing our recommendation algorithm, which could be thought of as a “black box”. We knew that persons with different credit score ranges, budget preferences, income, etc. would prefer different credit cards, but generalizing outputs to be statistically similar was at the core of our backend algorithm. Devising a solution to this involved ample research and randomized input dataset generation to train our model.

## Accomplishments that we're proud of

Overall, we are very happy to have been able to work with the Apple Vision Pro, considering it has not yet been released.
We’re proud of coming up with an innovative credit card recommendation algorithm and making the credit card rewards and application process more transparent.

## What we learned

Our team had a diverse mix of experience from different backgrounds. The key takeaway from this experience was the ability to cross-collaborate on different ends of the project, teaching one another new technologies of the software development process. Some of us delved into the intricacies of Google Cloud databases, others explored unfamiliar frontend frameworks, and some of us dived deep into some cool math for our backend.

CardMaster turned out to be rewarding for all - our users would reap its benefits, while for us, designing and developing it was an immensely gratifying experience. We’re very happy with our completed project!

## What's next for CardMaster

At HackHarvard, we used the opportunity to build CardMaster, particularly the backend services, to establish a foundation for a broader credit churning and optimization platform that we are developing. CardMaster churns credit cards, providing users for a call-to-action to improve their financial potential. The future phases of building CardMaster would address the management and optimized usage of a multi-card wallet for real-time transactions. Through these developments, our work will eventually improve the usage of credit cards at all stages of the churning process - before and after.

## Domain Name From GoDaddy

getrichwith.us

(for real)