WIRELESS COMMUNICATION EVOLUTION: EVALUATING NEXT-GENERATION PROXIMITY TRANSACTION TECHNOLOGY AND ITS APPLICATION IN SHORT-RANGE TRANSACTION SYSTEMS
CHIRANJEEVI UDARI
Engineering
October 2023
In order to shed light on the underlying patterns and components in the data, this study performed a thorough analysis of two datasets using Principal Component Analysis (PCA) and descriptive statistics. The first dataset, which consisted of five data points, initially showed a symmetrical distribution with values concentrated around an average of 18. This distribution was backed by low volatility and a tiny standard deviation of around 4.24. Five students' differences in academic strengths and weaknesses were found in the second dataset, which included student performance scores in a variety of courses. Notably, Student 4 performed worse in most disciplines, whereas Student 2 performed very well in math and physical education. Our comprehension of the second dataset was significantly improved by the use of PCA, which revealed hidden variables influencing total academic achievement. While particular educational judgments or conclusions need larger datasets and a more focused study focus, these findings together provide the groundwork for future research in the fields of education and performance evaluation. This study highlights the value of dimensionality reduction and statistical analysis in deciphering complex data structures and providing crucial information for future research and decision-making
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