Abstract:
"Food waste is a global problem that presents serious environmental, economic, and social issues. Food expiration dates are something that most homes are unaware of, which can result in needless waste. The author suggests a Machine Learning-Based Food Waste Reduction System as a solution to this problem. Users can enter food items and the corresponding expiration dates into the system, which then generates alerts for soon-to-expire items and suggests recipes tailored to utilize these items efficiently.
The method presented in this research paper combines user-inputted data and machine learning algorithms to properly anticipate food item expiration dates, thereby addressing the issue of food waste. Users can quickly enter the specifics of their food inventory through a simple interface, which allows the system to analyze the data and provide timely notifications for goods that are about to expire. Furthermore, the technology makes use of machine learning algorithms to suggest recipes that include soon-to-expire items, encouraging sustainable eating habits and reducing food waste. This project service seeks to reduce home food waste and contribute to a more sustainable food consumption ecosystem by providing users with personalized recommendations and actionable insights.
In this project a recipe dataset was obtained from Kaggle for training and testing with the RNN(Recurrent neural network) model which is optimal for a recommendation system. The RNN model comes with 91% accuracy in average when the dataset is trained."
