Global Food Waste Prediction (2018-2024): Trend Analysis and Random Forest Regression Model Development

Food waste remains a major global sustainability challenge due to its environmental, economic, and social impacts. Understanding food waste patterns and their contributing factors is essential for supporting effective mitigation strategies. This study aims to (1) investigate food waste trends and patterns through Exploratory Data Analysis (EDA) and (2) develop a predictive model for estimating total food waste using Random Forest Regression. The study utilizes a publicly available dataset containing 5,000 records from 20 countries, covering eight food categories over the period 2018–2024. The dataset includes variables such as food category, economic loss, population, average waste per capita, and household waste percentage. Exploratory analysis reveals variations in waste generation across food categories and countries, with fruits and vegetables contributing a substantial share of total waste. A Random Forest Regression model was developed and evaluated, achieving a coefficient of determination (R²) of 0.9582. In addition to predictive performance, the study highlights the importance of examining key contributing variables to better understand food waste patterns. The findings demonstrate the potential of machine learning techniques as decision-support tools for food waste analysis and management, while also acknowledging the limitations associated with secondary public datasets.

Keywords: food waste, random forest regression, machine learning, predictive analytics, sustainability