wellSpent

Inspiration

The inspiration for 'wellSpent' stems from a simple question: "Where's my money going?" We've all been there – the end of the month rolls around, and we're left wondering how we managed to spend so much. That's when we realized, what if we could visualize our spending patterns, set future financial goals, and understand our expenses better?

What it does

'wellSpent' is more than just an app; it's your pocket-sized financial advisor. At its core, it showcases a dynamic pie chart, giving users a quick snapshot of their expenses. But that's just the start. The app lists all transactions, ensuring you never miss where that extra dollar went. And with features like the travel planner, student debt planner, and other expenses planner, users can forecast, strategize, and optimize their financial futures.

How we built it

The app was constructed using a combination of Flutter for the frontend and Firebase for backend support. We utilized Python scripts to handle data processing, ensuring the pie charts were updated in real-time. Matplotlib and milvus were our go-to tools for the intricate visualization components and vector base components.Our group has harnessed the potential of vector representations to revolutionize data categorization, ensuring swifter searches and pinpoint sorting. By adeptly converting intricate data into vector spaces, we've streamlined the query process and paved the way for efficient trend detection. This approach stands out for its cost-effectiveness, significantly cutting down computational overhead and storage expenditures. In contexts demanding high scalability, like the banking sector, our vector database solutions don't just promise speed but also unmatched scalability. Our proficiency in managing enormous data volumes without a dent in performance marks us as the go-to choice for financial entities prioritizing cost-effectiveness and operational excellence.

Challenges we ran into

Integrating real-time data updating with Flutter posed a significant challenge. Ensuring the app was user-friendly while handling vast amounts of data was another obstacle we faced. Training our model to give precise future financial predictions without ample data was, no doubt, one of our most daunting tasks. We had a hard time calling API through our pie charts and putting real-time transaction data for our spending prediction model

Accomplishments that we're proud of

Creating a user-centric interface that's both intuitive and efficient tops our list. We're also immensely proud of the predictive features – like creating comprehensive tools which will tell you how you can save money even after a transaction along with other tools like customer trip planner already built using your spending transactions and trends. Using vector databases was our first time and we are very proud of our outcome

What we learned

Apart from the technical learnings around Flutter and data visualization tools, we grasped the importance of user feedback in shaping an app. We also understood the challenges of predictive modeling, especially when data is scarce.

What's next for wellSpent

While 'wellSpent' has made strides in personal finance management, there's still room for growth. Given more time and data, we aim to refine our model for enhanced accuracy. Additionally, we envision the app branching out to encompass smaller, niche sub-categories, offering users even more tailored financial insights and built more tools which help disabled and inclusive people.