The Application of Data Science on Food Waste Problem: A Preliminary Work
DOI:
https://doi.org/10.31436/ijpcc.v9i1.352Keywords:
food waste, sustainable development goals, data science, exploratory data analysis, Southeast Asia, undernourishment, food typeAbstract
Food waste refers to food that is fit for human consumption, but is discarded after it is kept past its expiration date or allowed to spoil due to factors like food spoilage, market surplus, and individual shopping and eating habits. It is a common problem worldwide, including in Malaysia. In reality, one-third of the total food generated is wasted or lost annually, and at least 820 million people worldwide do not have sufficient food. This study attempts to observe the current trends in the amount of food waste generated in Malaysia so that the public awareness can be increased. This paper also wishes to study about the undernourishment crisis for lowering the extreme hunger issues and to reduce the food losses especially rice, along the production and supply chain. This study indirectly aligns with sustainable development goals (SDG) related to zero hunger, and responsible consumption and production. Initially, the data science processes were implemented including data collection, data pre-processing, data cleaning, data exploration and selection, data wrangling, and exploratory data analysis (EDA). In Japan, the food waste issue was not given enough attention. It reflects the significant upswing in its early graph, which then began to decline. On the other hand, the food waste issue in Korea initially exhibited a sharp surge before a progressive drop. Regarding the undernourishment issue, almost all Southeast Asian nations nowadays are putting a lot of effort into eradicating or drastically lowering undernutrition among their citizens compared to the past. Rice is the common food type in Southeast Asia, which means it gets discarded frequently. The residents of Southeast Asia regard rice as a main food source.
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