A driver’s license bureau maintains a database of licensed drivers. State whether each of the following represents data or metadata. If it represents data, state whether it is structured or unstructured data. If it represents metadata, state whether it is a fact describing a property of data or a fact describing the context of data. An image of the driver’s fingerprint The make and serial number of the scanning device that was used to scan the fingerprint The resolution (in megapixels) of the camera that was used to photograph the driver The fact that the driver’s birth date must precede today’s date by at least 16 years

A driver’s license bureau maintains a database of licensed drivers. State whether each of the following represents data or metadata. If it represents data, state whether it is structured or unstructured data. If it represents metadata, state whether it is a fact describing a property of data or a fact describing the context of data. An image of the driver’s fingerprint The make and serial number of the scanning device that was used to scan the fingerprint The resolution (in megapixels) of the camera that was used to photograph the driver The fact that the driver’s birth date must precede today’s date by at least 16 years

Fundamentals of Information Systems

9th Edition

ISBN:9781337097536

Author:Ralph Stair, George Reynolds

Publisher:Ralph Stair, George Reynolds

Chapter3: Database Systems And Big Data

Section: Chapter Questions

Problem 14SAT

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Example: an airline's database. If airlines improve, an unserialized technology may be acceptable.
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Case Study:This case study considers the Credit approval dataset. This file concerns credit card applications. Allattribute names and values have been changed to meaningless symbols to protect confidentiality ofthe data.This dataset is interesting because there is a good mix of attributes -- continuous, nominal with smallnumbers of values, and nominal with larger numbers of values. There are also a few missing values.Attribute Information:A1: b, a.A2: continuous.A3: continuous.A4: u, y, l, t.A5: g, p, gg.A6: c, d, cc, i, j, k, m, r, q, w, x, e, aa, ff.A7: v, h, bb, j, n, z, dd, ff, o.A8: continuous.A9: t, f.A10: t, f.A11: continuous.A12: t, f.A13: g, p, s.A14: continuous.A15: continuous.A16: +,- (class attribute)Question: Apply the KNN algorithm to predict the acceptance of rejection of a credit cardapplication. Follow the same strategy used in chapter 7. please solve it using RStudio