The food and beverage industry significantly impacts the global economy, subject to various influential factors. This study aims to develop an AI-powered model to enhance the understanding of regional food and beverage sales dynamics with a primary goal of globalizing food items based on ingredient consumption metrics. Methodologically, this research employs Long-Short Term Memory (LSTM) architecture RNN to create a framework to predict food item performance using historical time series data. The model’s hyperparameters are optimized using genetic algorithm (GA), resulting in higher accuracy and a more flexible model suitable for growing and real-time data. Data preprocessing involves comprehensive analysis, cleansing, and feature engineering, including the use of gradient boosting models with K-fold cross-validation for revenue prediction. Historical sales data from 1995 to 2014, sourced from Kaggle open-source database, are prepared to capture temporal dependencies using sliding window techniques, making it suitable for LSTM model input. Evaluation metrics reveal the hybrid LSTM-GA model’s efficacy, outperforming baseline LSTM with an MSE reduction from 0.045 to 0.029. Ultimately, this research underscores the development of a model that harnesses historical sales data and sophisticated machine learning techniques to forecast food item sales growth, empowering informed investment decisions and strategic expansions in the global food market.
Rohith Rao Yannamaneni
Globalizing Food Items Based on Ingredient Consumption [PDF]
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