PersonalisedHelloFresh

Inspiration

everyone's taste in food is unique

What it does

PersonalisedHelloFresh revolutionizes the meal planning experience by providing tailor-made recipe recommendations. Using advanced machine learning algorithms, it analyzes users' taste preferences, dietary restrictions, and past selections to suggest recipes that not only align with their dietary needs but also tantalize their taste buds.

How we built it

We employed Python for backend and frontend development via streamlit framework , integrating machine learning models for predictive analytics.

The dataset that we used was from Kaggle, the link to the dataset: https://www.kaggle.com/datasets/shuyangli94/food-com-recipes-and-user-interactions/?select=RAW_recipes.csv

Challenges we ran into

Our main challenge was to integrate frontend with backend.

Accomplishments that we're proud of

We almost made it running.

What we learned

we learned how to integrate backend with frontend.

What's next for PersonalisedHelloFresh

1 - The next step would definitely be to make the website fully functional. 2 - We used K nearest neighbour algorithm but we found out later that reinforcement learning algorithm would have been the better choice. So this will be our next step, to implement RL algorithm.