Hello, below is my code for a linear regression model using Python. I am trying to include 2 thetas using the gradient descent function and plot the graph of the optimized slope and points. I have included the graph that my code currently outputs, but the slope and points do not align. If someone could help me rewrite this code to include both thetas and optimized slope and points, I would appreciate it. Thank You.

from sklearn.datasets import load_diabetes

from sklearn.metrics import mean_squared_error

import numpy as np

# Load the diabetes dataset

diabetes = load_diabetes()

x = diabetes.data

y = diabetes.target

#Expand and verify the data shape

y = np.expand_dims(y, 1)

data = np.append(x, y, 1)

#Random Shuffle

np.random.shuffle(data)

#Split Method

total_sample = len(data)

train = data[:int(total_sample*0.7)]

dev= data[int(total_sample*0.7):int(total_sample*0.85)]

test = data[int(total_sample*0.85):]

#Verify Features and Labels

def get_features_and_labels(data):

features = data[:, :-1]

labels = data[:, -1]

return features, labels

train_x, train_y = get_features_and_labels(train)

dev_x, dev_y = get_features_and_labels(dev)

test_x, test_y = get_features_and_labels(test)

# Define the gradient descent function

def gradient_descent(X, y, alpha, num_iters):

m = len(y)

theta = np.zeros((X.shape[1], 1))

for i in range(num_iters):

y_pred = X @ theta

theta -= (alpha / m) * (X.T @ (y_pred - y.reshape(-1, 1)))

return theta

# Define the cost function

def cost_function(X, y, theta):

m = y.shape[0]

y_pred = X @ theta

J = (1 / (2 * m)) * np.dot((y_pred - y.reshape(-1, 1)).T, y_pred - y.reshape(-1, 1))

return J

# Train and evaluate 10 univariate linear regression models, one for each feature

best_mse = float('inf')

best_theta = None

best_feature = None

for feature in range(train_x.shape[1]):

X = train_x[:, [feature]]

alpha = 0.01

num_iters = 1000

theta = gradient_descent(X, train_y, alpha, num_iters)

predictions = dev_x[:, feature].reshape(-1, 1) @ theta

mse = mean_squared_error(dev_y, predictions)

if mse

best_mse = mse

best_theta = theta

best_feature = feature

import matplotlib.pyplot as plt

# Plot the data and the regression line for the best feature

X = train_x[:, best_feature].reshape(-1, 1)

Y = train_y

plt.scatter(X, Y)

plt.plot(X, X @ best_theta, color='red')

plt.xlabel('Feature {}'.format(best_feature))

plt.ylabel('Target')

plt.show()

Feature 2