# Using numpy to enter noise into data

I am new to data science and have to generate 200 numbers from a uniform distribution
set this as x and generate y data using x and injecting noise from the gaussian distribution
y = 12x-4 + noise

My Approach:
x = numpy.random.rand(200) --> This will generate 200 numbers form a uniform distribution
I am not sure hot to inject noise from the guassian distribution
probably it's like z = numpy.random.randn(200) and y = 12 * x - 4 + z
Is that a correct way to inject noise?

Yes. numpy.random.randn(n) will generate an array of random numbers (generated by the normal distribution centered at 0) of size n. So just do:
import numpy as np