PopUpBiz

Helping Pop Up Businesses Thrive During The Pandemic.

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Inspiration

Over the pandemic, many pop-up businesses have been financially burdened due to the lack of consumers and have been forced to deliver. However, as restrictions start to ease, many Pop-up businesses have opened in locations where transportation by walking/biking is impossible: this may cause city gridlock problems, which in fact is against the goal of net-zero emissions by 2030. Hence, PopUpBiz aims to help pop-up businesses thrive during the pandemic by providing the business with the best location in a given city based on information such as demographics/region, trends, and whether the location is within a walkable/bikable distance for consumers to travel in order to not only benefit the business itself but also solve the city's gridlock problem that many pop-up businesses cause!

What it does

PopUpBiz provides business owners with optimal and healthy pop-up business locations. PopUpBiz first takes in information about the business such as the type, preferred location, and radius preference. The business owner is then provided with an optimal location radius using a heuristics machine learning model on walk paths and biking paths by getting the K shortest loopless path along with a time series trend tracking model which locates the area in the city with the most possible consumers. PopUpBiz also enhances its customer experience by featuring an AI customer service chatbot coded using Dialogflow API, NLP, and node.js; the chatbot can also be used as an emotional support agent!

How we built it

blob:https://devpost.com/66a5649b-8bae-4203-8eb2-244b1c8bd219 We built the UI using Figma and built the frontend using HTML and css. The backend was built using python and node.js. We used google cloud's maps API for displaying the map along with the polygon along with google cloud's Dialogflow API for the customer experience chatbot. For the heuristics machine learning model and product trend tracking time series analysis, we attempted to use TensorFlow and python.

Challenges we ran into

We ran into a number of challenges. One includes communication between members and time zone differences. Due to certain time constraints, we were unable to fully implement our machine learning models into our actual web application despite having a successful raw run in the backend.

Accomplishments that we're proud of

This was the first hackathon for two of our members which was a great accomplishment for them. Moreover, we are really proud of the complexity of our project.

What we learned

Through our journey of coding our application, we learned about natural language processing, heuristics machine learning, maps api, dialogflow api, and about how to integrate certain technologies we used.

What's next for PopUpBiz

In the future, we plan to build a mobile application.

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