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A Closer Look at the Key Attributes of Breakfast Cereals: Comprehensive Data Analysis
- 1 Metro International Secondary Academy, Ontario Markham, Canada, L6B-L6G
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Abstract
This paper uses advanced techniques for data visualization in order to provide clear guidance for both consumers and manufacturers within the breakfast cereal industry. The research primarily focuses on analyzing key characteristics such as caloric content, fiber content, and sugar levels across various renowned brands of cereal including Larana Inc., General Mills, Kellogg's, and Nestlé. By combining the powers of Python as a programming language along with the Pandas, Matplotlib, and Seaborn libraries, this analysis delves into consumer trends, general industry patterns, and nutritional concerns. Through a meticulous examination of these factors, this study aims to provide valuable insights that can aid stakeholders in making informed decisions towards promoting healthier product development. The findings obtained from this research act as a testament to the critical function that data-driven insights play in the formation of consumer decisions toward more nutritious options. These results not only contributes important knowledge for stakeholders but also pave the way for potential innovations in data analytics within the cereal market. Overall, by employing cutting-edge tools and methodologies in data visualization combined with an objective approach to examining relevant characteristics of breakfast cereals from prominent brands, this paper strives to enhance understanding within the industry while facilitating advancements in data analytics practices.
Keywords
Breakfast cereals, data analysis, nutritional characteristics, consumer insights.
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Cite this article
Zeng,B. (2024). A Closer Look at the Key Attributes of Breakfast Cereals: Comprehensive Data Analysis. Applied and Computational Engineering,96,56-60.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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