Day 5 - Linear Regression Numpy Code and Python

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Python


 In the course today we learned about the following concepts:
  1. Lists 
  2. Dictionaries
  3. Tuples
  4. Sets
  5. Booleans
  6. Dealing with Files in Python
  7. Iterating in a file

Linear Regression Numpy Code

The code was completed and it was:

import math

beta = beta_zero
cost_diff = 100

rmse =-1

for i in range(10000):
    old_rmse = rmse
    y_hatnew = x_data.dot(beta)
        
    y_diff =y_true.reshape(len(x_inputs),1) - y_hatnew
        
    rmse = math.sqrt(y_diff.T.dot(y_diff)/x_data.shape[0])
    print(i,":",rmse)
        
    if abs(rmse-old_rmse) < 0.000000000001:
        break
    
    derivative = 2*y_diff.T.dot(x_data)/x_data.shape[0]
    beta = beta+step*derivative.T
print(beta) 

The next task given to us was to implement the sklearn function of Linear Regression and to compare the results of our version and the sklearn function.

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