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1. Using numpy sample 200 numbers from a uniform distribution and store it into variable x.  Generate y data using x and injecting noise from the gaussian distribution (i.e. y = 12x-4 + noise). Using matplotlib plot the data samples, configuring axis so all samples are clearly visible. Split the data into training (80%) and testing (20%) sets using scikit-learn Note: Ensure to set the random seed to reproduce same random number during different execution times.

1. Using numpy sample 200 numbers from a uniform distribution and store it into variable x.  Generate y data using x and injecting noise from the gaussian distribution (i.e. y = 12x-4 + noise). Using matplotlib plot the data samples, configuring axis so all samples are clearly visible. Split the data into training (80%) and testing (20%) sets using scikit-learn Note: Ensure to set the random seed to reproduce same random number during different execution times.

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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1. Using numpy sample 200 numbers from a uniform distribution and store it into variable x.  Generate y data using x and injecting noise from the gaussian distribution (i.e. y = 12x-4 + noise). Using matplotlib plot the data samples, configuring axis so all samples are clearly visible. Split the data into training (80%) and testing (20%) sets using scikit-learn

Note:

  • Ensure to set the random seed to reproduce same random number during different execution times.
import numpy as np mport numpy.random as rnd np.random.seed (10)
Transcribed Image Text:import numpy as np mport numpy.random as rnd np.random.seed (10)
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