Add Linear Regression

[Kubha99]

No description provided.

[Kubha99]

I saw that Linear Regression algorithm was missing under ML module. Will try to implement it.

[josezy13]

close

[josezy13]

y

[rmsvivek]

LR algorith is missing

[jaki729]

This code represents a basic implementation of simple linear regression using gradient descent. Replace X and y with your dataset. The code iterates through multiple epochs, calculating predictions, errors, gradients, and updating weights to minimize the error.

# Simple Linear Regression using Gradient Descent

# Sample data
X = np.array([1, 2, 3, 4, 5])  # Input feature
y = np.array([3, 4, 2, 5, 6])  # Target variable

# Hyperparameters
learning_rate = 0.01
epochs = 1000

# Initialize slope (weight) and intercept (bias)
m = 0  # Initial slope
c = 0  # Initial intercept

n = float(len(X))  # Number of elements in X

# Gradient Descent
for i in range(epochs):
    # Predictions
    y_pred = m * X + c

    # Error calculation
    error = np.mean((y_pred - y)**2)

    # Gradient calculation
    gradient_m = (-2/n) * np.sum(X * (y - y_pred))
    gradient_c = (-2/n) * np.sum(y - y_pred)

    # Update weights
    m -= learning_rate * gradient_m
    c -= learning_rate * gradient_c

# Final slope and intercept
print("Slope:", m)
print("Intercept:", c)