The tasks are relevant to the dataset "College.csv" college.csv file at the path: https://drive.google.com/file/d/1StNKOnI0IDQKgZ35b-VZf1_5a_Dono-N/view?usp=sharing request code python : K nearest neighbor: a. Create a binary attribute Apps01 that contains a 1 if Apps contains a value equal to or above its median, and a 0 if Apps contains a value below its median. Create a single data set d containing both Apps01 and the other College features. Split the data into d.train training set and d.test test set 80:20. b. Perform k-NN with several values of k in order to predict Apps01 using all the features. c. Display the comparison of validation error rate for different values of k.
The tasks are relevant to the dataset "College.csv" college.csv file at the path: https://drive.google.com/file/d/1StNKOnI0IDQKgZ35b-VZf1_5a_Dono-N/view?usp=sharing request code python : K nearest neighbor: a. Create a binary attribute Apps01 that contains a 1 if Apps contains a value equal to or above its median, and a 0 if Apps contains a value below its median. Create a single data set d containing both Apps01 and the other College features. Split the data into d.train training set and d.test test set 80:20. b. Perform k-NN with several values of k in order to predict Apps01 using all the features. c. Display the comparison of validation error rate for different values of k.
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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The tasks are relevant to the dataset "College.csv"
college.csv file at the path: https://drive.google.com/file/d/1StNKOnI0IDQKgZ35b-VZf1_5a_Dono-N/view?usp=sharing
request code python : K nearest neighbor:
a. Create a binary attribute Apps01 that contains a 1 if Apps contains a value equal to or above its median, and a 0 if Apps contains a value below its median. Create a single data set d containing both Apps01 and the other College features. Split the data into d.train training set and d.test test set 80:20.
b. Perform k-NN with several values of k in order to predict Apps01 using all the features.
c. Display the comparison of validation error rate for different values of k.
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