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What is PCA (Principal Component Analysis)

Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
PCA is a dimension-reduction tool that can be used to reduce a broad set of variables to a small collection that contains most of the information in the massive game.
Published in Chapter:
Descriptive Data Analytics on Dinesafe Data for Food Assessment and Evaluation Using R Programming Language: A Case Study on Toronto's Dinesafe Inspection and Disclosure System
Ajinkya Kunjir (Lakehead University, Canada), Jugal Shah (Lakehead University, Canada), and Vikas Trikha (Lakehead University, Canada)
DOI: 10.4018/978-1-7998-3053-5.ch025
Abstract
In the digital era of the 21st century, data analytics (DA) can be highlighted as 'finding conclusions based on observations' or unique knowledge discovery from data (KDD) in form of patterns and visualizations for ease of understanding. The city of Toronto consists of thousands of food chains, restaurants, bars based all over the streets of the city. Dinesafe is an agency-based inspection system monitored by the provincial and municipal regulations and ran by the Ministry of Health, Ontario. This chapter proposes an efficient descriptive data analytics on the Dinesafe data provided by the Health Ministry of Toronto, Ontario using an open-source data programming framework like R. The data is publicly available for all the researchers and motivates the practitioners for conveying the results to the ministry for betterment of the people of Toronto. The chapter will also shed light on the methodology, visualization, types and share the results from the work executed on R.
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