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Big Data Analytics and Visualization for Food Health Status Determination Using Bigmart Data
Abstract
Being premature, the traditional data visualization techniques suffer from several challenges and lack the ability to handle a huge amount of data, particularly in gigabytes and terabytes. In this research, we propose an R-tool and data analytics framework for handling a huge amount of commercial market stored data and discover knowledge patterns from the dataset for conveying the derived conclusion. In this chapter, we elaborate on pre-processing a commercial market dataset using the R tool and its packages for information and visual analytics. We suggest a recommendation system based on the data which identifies if the food entry inserted into the database is hygienic or non-hygienic based on the quality preserved attributes. For a precise recommendation system with strong predictive accuracy, we will put emphasis on Algorithms such as J48 or Naive Bayes and utilize the one who outclasses the comparison based on accuracy. Such a system, when combined with R language, can be potentially used for enhanced decision making.
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