## Inspiration

The inspiration for this project comes from a brainstorming session that ended up in a conclusion that marketing campaigns are sporadically ran and could benefit from heavy optimizing.

## What it does

This program pinpoints previously untargete

## How we built it

This project was built using a number of different softwares. Our main coding language included Javascript. However this project would not have been possible without the help of Reactjs, Node.js, vitech api, Ezri api, leaflet, HTML, CSS

## Challenges we ran into

Our main challenge was trying to figure out what we define as an "area" within our map. This was a particularly challenging task as state-wide "areas" would've been too clumpy and defining cities as "areas" caused us to run into issues.

## Accomplishments that we're proud of

## What we learned

## What's next for Optimising Marketing Solutions
## Inspiration:

The inspiration behind SNAPMap came from the need to improve accessibility to essential services for low-income communities who rely on SNAP and EBT benefits. Finding stores that accept these benefits can be challenging, especially in underserved areas with little tech access. SNAPMap aims to make this process easier by providing a simple, user-friendly solution for these communities to quickly locate nearby grocery, convenience, and food stores that accept SNAP and EBT across the U.S.

## What it does:

SNAPMap helps users find grocery, convenience, and food stores across the U.S. that accept SNAP and EBT benefits. The app allows users to input a zip code and filter by store type, such as convenience stores, grocery stores, or supermarkets, to find locations near them. The interactive map visualizes the stores, making it easy to see where they can shop using their benefits. By providing this information in a user-friendly format, SNAPMap simplifies the process of locating SNAP/EBT-friendly stores, ensuring that users can easily access the food they need in their community despite economic and time hardships.

## How we built it:

We built SNAPMap using a combination of Python for backend development and data handling, and JavaScript for the interactive map. We sourced the dataset of SNAP and EBT-accepting locations from the United States Department of Agriculture (USDA) and used geocoding services to map them accurately. For the frontend, we used a clean, responsive design to ensure easy navigation. The filter functionality allows users to input their zip code and select the type of store they’re looking for, making the experience personalized and efficient.

## Challenges we ran into:

One of the main challenges was managing a large dataset and ensuring its accuracy and performance within the app. We also faced challenges in connecting the back-end which was written with JavaScript and the front end which was written with Python. Additionally, implementing a robust filtering system that provides accurate results without overwhelming the user with too many options required careful planning and testing.

## Accomplishments that we're proud of:

One of our proudest accomplishments is creating an intuitive and accessible tool that has the potential to make a real impact for individuals and families relying on SNAP/EBT benefits. We successfully built an app that is easy to navigate, allowing users to quickly find stores that meet their needs based on location and store type—making sure accessibility is no longer a barrier. We’re also proud of the seamless integration of our interactive map and filtering features, which required handling a large dataset while ensuring a smooth user experience. Despite initial challenges, we were able to implement a user-friendly interface that works well without compromising on functionality or speed. Overall, we’re proud that we turned an idea into a functional, impactful app that addresses a real need in underserved communities.

## What we learned:

Throughout this project, we learned a lot about the complexities of mapping large datasets, working with geolocation tools, and managing user input for effective data filtering. We also learned how to ensure that a front end and back end can communicate with each other despite being written in different languages. Additionally, we gained valuable insight into the importance of clean data, usability, and providing a seamless user experience, particularly for audiences that may not be tech-savvy.

## What's next for SNAPMap:

Looking ahead, we plan to expand SNAPMap’s features by introducing meal planning ideas based on the food available at stores that accept SNAP and EBT. Users will be able to see suggested meal plans tailored to their dietary preferences and the types of stores they’re searching for. By integrating data on available food items, SNAPMap could provide budget-friendly, nutritious meal options that align with SNAP benefits, making the shopping experience even more convenient and empowering users to make informed decisions about their food choices—-and making life easier for communities disadvantaged with economic and time constraints.
## Inspiration:

The inspiration behind Pisces stemmed from our collective frustration with the time-consuming and often tedious process of creating marketing materials from scratch. We envisioned a tool that could streamline this process, allowing marketers to focus on strategy rather than mundane tasks.

## Learning:

Throughout the development of Pisces, we learned the intricate nuances of natural language processing and machine learning algorithms. We delved into the psychology of marketing, understanding how to tailor content to specific target audiences effectively.

## Building:

We started by gathering a diverse team with expertise in marketing, software development, and machine learning. Collaborating closely, we designed Pisces to utilize cutting-edge algorithms to analyze input data and generate high-quality marketing materials autonomously.

## Challenges:

One of the main challenges we faced was training the machine learning models to accurately understand and interpret product descriptions. We also encountered hurdles in fine-tuning the algorithms to generate diverse and engaging content consistently. Despite the challenges, our dedication and passion for innovation drove us forward. Pisces is not just a project; it's a testament to our perseverance and commitment to revolutionizing the marketing industry.

## Interested to Learn More?

**Read from the PROS!**

Pisces has the power to transform marketing teams by reducing the need for extensive manpower. With traditional methods, it might take a team of 50 individuals to create comprehensive marketing campaigns. However, with Pisces, this workforce can be streamlined to just 5 people or even less. Imagine the time saved by automating the creation of ads, videos, and audience insights! Instead of spending weeks on brainstorming sessions and content creation, marketers can now allocate their time more strategically, focusing on refining their strategies and analyzing campaign performance. This tool isn't just a time-saver; it's a game-changer for the future of marketing. By harnessing the efficiency of Pisces, companies can launch campaigns faster, adapt to market trends more seamlessly, and ultimately, achieve greater success in their marketing endeavors. Pisces can be effectively used across various industries and marketing verticals. Whether you're a small startup looking to establish your brand presence or a multinational corporation aiming to scale your marketing efforts globally, Pisces empowers you to create compelling campaigns with minimal effort and maximum impact.

## Demos

Walkthrough (bad compression): [YouTube Link](https://www.youtube.com/watch?v=VGiHuQ7Ha9w)
Muted Demo (for ui/ux purposes): [YouTube Link](https://youtu.be/56MRUErwfPc)