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use PYHTON Now it’s your turn. We have prepared our data. Now the next step is to implement the KNN algorithm 1. Create a function named KNNClassifier that take input the value of K. 2. Use Euclidean Distance measure to find the distance between two datapoints. (Create a separate function that will take two instances and return the distance) 3. Create a function Predict and make predictions using the function we created for KNN classification. Call the function with different values of the K and print actual and predicted values.

use PYHTON Now it’s your turn. We have prepared our data. Now the next step is to implement the KNN algorithm 1. Create a function named KNNClassifier that take input the value of K. 2. Use Euclidean Distance measure to find the distance between two datapoints. (Create a separate function that will take two instances and return the distance) 3. Create a function Predict and make predictions using the function we created for KNN classification. Call the function with different values of the K and print actual and predicted values.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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use PYHTON

Now it’s your turn. We have prepared our data. Now the next step is to implement
the KNN algorithm
1. Create a function named KNNClassifier that take input the value of K.
2. Use Euclidean Distance measure to find the distance between two
datapoints. (Create a separate function that will take two instances and
return the distance)
3. Create a function Predict and make predictions using the function we created
for KNN classification. Call the function with different values of the K and
print actual and predicted values.

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