Forkast

Inspiration 🙇‍♀️

Our inspiration for Forkast came from the challenges faced by restaurants and venues in predicting future order volumes. Accurate forecasting is critical for optimizing operations, reducing waste, and maximizing efficiency. The ability to predict order volumes based on key factors like restaurant type, location, and time period can make a significant difference in improving business outcomes. We set out to create a tool that empowers businesses to make data-driven decisions and plan for success.

What it does 👩‍💻

Forkast is a machine learning-powered tool that predicts future order volumes for restaurants based on user inputs. By selecting or typing in key parameters—such as the type of restaurant, city, and country—users can generate accurate forecasts for their business. This tool helps restaurant owners and managers optimize their operations, including staffing, inventory, and resource allocation, to improve efficiency and profitability.

How we built it ⚙️

We built Forkast using a combination of machine learning and time-series analysis with Prophet, a popular forecasting tool. The user interface was developed using Streamlit, which allowed us to quickly prototype an interactive and user-friendly experience. We trained the model using historical order data and fine-tuned it to account for different restaurant concepts and locations, leveraging sci-kit learn, numpy, and pandas. The app uses machine learning algorithms to generate accurate order predictions, empowering users to make more informed decisions.

Challenges we ran into 😰

One of our biggest challenges was getting several team members up to speed with machine learning, which required extra time and effort to ensure everyone was on the same page. We also faced time management challenges, as balancing project deadlines with learning curves was difficult. Additionally, we aimed to make the project more innovative by developing an intuitive frontend to complement the backend, which presented its own set of design and implementation hurdles. Despite these challenges, we successfully collaborated and adapted to deliver a cohesive solution.

Accomplishments that we're proud of ⭐️

We’re proud of how well the app integrates machine learning with user-friendly design, making powerful forecasting accessible to restaurant owners. The ability to generate accurate forecasts with just a few inputs showcases how data-driven tools can drive real-world improvements. We’re also proud of how quickly we built the application, leveraging Streamlit for rapid development and making machine learning accessible to users without requiring technical expertise.

What we learned 🔎

Through this project, we learned how to implement machine learning models in a real-world application, balancing accuracy with performance. We also gained experience in working with time-series data and learned how to preprocess data effectively for training models. Additionally, we honed our skills in designing intuitive, user-friendly interfaces and ensuring that the app provides actionable insights without overwhelming users with complexity.

What's next for Forkast 👀

In the future, we plan to expand Forkast’s capabilities by incorporating more factors into the forecasting model, such as weather conditions, holidays, and other external variables that may affect order volumes. We also aim to improve the app’s scalability and performance to handle larger datasets and more complex forecasting scenarios. Additionally, we’re exploring ways to integrate Forkast with existing restaurant management systems for seamless, real-time forecasting. Ultimately, we want to make Forkast a go-to solution for businesses looking to optimize their operations through predictive analytics.