Please don't copy from chegg and course hero A lecturer has missed to mark one exam paper – Isabella's. He doesn't have time to mark it and decidesto use the k-Nearest Neighbor algorithm to predict Isabella's exam grade based on her marks forAssignment 1 and Assignment 2 and the performance of the other students in the course, shown in thetable below:student Assignment 1 Assignment 2123456605040205550854020307590Exam gradeHDPFFCRDIsabella's assignment marks are: Assignment 1 = 65 and Assignment 2 = 80. What will be thepredicted exam grade for Isabella using 1-Nearest Neighbor with Manhattan distance? Show yourcalculations.No need to normalize the data, all marks are measured on the same scale – from 0 to 100.

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A lecturer has missed to mark one exam paper - Isabella's. He doesn't have time to mark it and decides to use the k-Nearest Neighbor algorithm to predict Isabella's exam grade based on her marks for Assignment 1 and Assignment 2 and the performance of the other students in the course, shown in the table below: student Assignment 1 1 2 3 4 5 6 60 50 40 20 55 50 Assignment 2 85 40 20 30 75 90 Exam grade HD P F F CR D = Isabella’s assignment marks are: Assignment 1 65 and Assignment 2 = 80. What will be the predicted exam grade for Isabella using 1-Nearest Neighbor with Manhattan distance? Show your calculations. No need to normalize the data, all marks are measured on the same scale - from 0 to 100.

A lecturer has missed to mark one exam paper - Isabella's. He doesn't have time to mark it and decides to use the k-Nearest Neighbor algorithm to predict Isabella's exam grade based on her marks for Assignment 1 and Assignment 2 and the performance of the other students in the course, shown in the table below: student Assignment 1 1 2 3 4 5 6 60 50 40 20 55 50 Assignment 2 85 40 20 30 75 90 Exam grade HD P F F CR D = Isabella’s assignment marks are: Assignment 1 65 and Assignment 2 = 80. What will be the predicted exam grade for Isabella using 1-Nearest Neighbor with Manhattan distance? Show your calculations. No need to normalize the data, all marks are measured on the same scale - from 0 to 100.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Concept explainers

Computational Systems

The computation systems can be defined as the systems that are capable of solving a problem that includes calculations either mathematical or logical, and are able to produce the result as an output. For example, a simple calculator that is given a set of numbers and operators as input and produces the result as an output can be defined as a computational system. The above example is a simple illustration only, the computational systems range from simple devices to complex devices such as the systems that control the SpaceX companies, Starlink satellites which are being quite recently seen in the skies at many places globally.

Question

Please don't copy from chegg and course hero

A lecturer has missed to mark one exam paper – Isabella's. He doesn't have time to mark it and decides
to use the k-Nearest Neighbor algorithm to predict Isabella's exam grade based on her marks for
Assignment 1 and Assignment 2 and the performance of the other students in the course, shown in the
table below:
student Assignment 1 Assignment 2
1
2
3
4
5
6
60
50
40
20
55
50
85
40
20
30
75
90
Exam grade
HD
P
F
F
CR
D
Isabella's assignment marks are: Assignment 1 = 65 and Assignment 2 = 80. What will be the
predicted exam grade for Isabella using 1-Nearest Neighbor with Manhattan distance? Show your
calculations.
No need to normalize the data, all marks are measured on the same scale – from 0 to 100.

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