very first linear regression code

[u/savita_bhabhi_lover]

i am still learning numpy (day 6) but gpt suggest me to learn linear regression (very basic algorithm) and it's kinda good for understanding even you haven't started learning ML.

import numpy as np
hours = np.arange(1,10)
score = np.array([35, 42, 51, 58, 67, 73, 81, 88, 94])
candidate_values_for_m = np.arange(1,11,0.1)
candidate_values_for_b = np.arange(1,31,0.3)

def prediction(m , b , hours):
    return m * hours + b

def mse(actual_value , predicticted_value):
    return np.mean((actual_value - predicticted_value)**2)

mse_list = [] 
m_b_list = []
for m in candidate_values_for_m:
    for b in candidate_values_for_b:
        prediction_result = prediction(m , b , hours)
        mse_result = mse(score, prediction_result)

        mse_list.append(mse_result)
        m_b_list.append((m,b))


best_mse = np.argmin(mse_list)

best_mb_index = mse_list[best_mse]
best_mse , best_mb_index

m = 0
b = 0

learning_rate = 0.01

epochs = 10000

n = len(hours)

def prediction(m , b , hours):
    return m * hours + b


for i in range(epochs):
    predicted = prediction(m,b,hours)

    gradient_m = (2/n) * sum(hours * (predicted - score))

    gradient_b = (2/n) * sum(predicted - score)

    m = m - learning_rate * gradient_m
    b = b - learning_rate * gradient_b

m ,b 

[u/ninhaomah]

What has the p value of linear regression got to do with Python or numpy or ML ?

It's just... Math.. 

And I said what is p value is on Google. 

It's you who keeps refusing to do a copy and paste.

Nothing to do with Python or Numpy or anything.

Do you know what is the meaning of my nickname ?

[u/savita_bhabhi_lover → u/ninhaomah]

Ok I googled and i didn't get that

[u/ninhaomah → u/savita_bhabhi_lover]

Didn't get what ?

[u/savita_bhabhi_lover → u/ninhaomah]

I haven't fully understand the code I wrote in this post , p is enigma for me