Abstract:
"In a world where dietary needs vary significantly based on factors such as health status, exercise
levels, and personal objectives, achieving tailored dietary goals can prove challenging. The
abundance of recipes available online further complicates matters, as many may not align with
individual nutritional requirements. This discrepancy underscores the need for a solution that can
generate personalized recipes based on user input and preferences, addressing the unique dietary
needs and preferences of everyone.
To tackle this problem, developing an innovative application leveraging a novel machine
learning algorithm is ideal. It involved gathering user input on ingredients they have on hand and
their dietary preferences. Then employed a machine learning algorithm to analyze this input and
generate personalized recipe recommendations tailored to each user's specific needs. This
approach allowed us to provide users with a curated selection of recipes that align with their
dietary requirements and preferences, offering a seamless and efficient solution to the challenge
of finding suitable recipes in the middle of the vast array of options available online.
Initial results demonstrate promising outcomes, with the machine learning model achieving high
accuracy in classifying and recommending recipes based on user input. The pre-processing of
datasets involves cleaning and standardizing recipe and ingredient data, while training the model
involves optimizing parameters and fine-tuning algorithms to enhance predictive performance.
With the initial implementation of the methodology, significant advancements in personalized
nutrition and dietary management were anticipated."
