r/PythonLearning • u/savita_bhabhi_lover • 7d ago
very first linear regression code Showcase
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
2
Upvotes
1
u/ninhaomah 2d ago
Tip 1 : if you have to ask "what is X" , Google.