find the least squared error in python

main.py
import numpy as np

# Step 1: Define x and y arrays
x = np.array([1, 2, 3, 4, 5])
y = np.array([1, 3, 2, 3, 5])

# Step 2: Perform linear regression using np.polyfit
slope, intercept = np.polyfit(x, y, 1)

# Step 3: Calculate predicted values of y using the regression equation
y_pred = slope * x + intercept

# Step 4: Calculate residuals
residuals = y_pred - y

# Step 5: Find the sum of squared residuals
sum_sq_err = np.sum(residuals**2)

# Step 6: Optimize sum of squared residuals using np.optimize.minimize_scalar
def sum_sq_err_func(slope, x, y):
    y_pred = slope * x + intercept
    residuals = y_pred - y
    return np.sum(residuals**2)

opt_result = np.optimize.minimize_scalar(sum_sq_err_func, args=(x, y))

# The optimized slope is:
opt_slope = opt_result.x
773 chars
29 lines