feat: add multiple linear regression implementation closes #2691

exercises/1000_programs/medium/multiple_linear_regression.py

@@ -0,0 +1,31 @@
+import numpy as np
+
+def multiple_linear_regression(X, y, lr=0.01, epochs=1000):
+    """
+    Implements Multiple Linear Regression using Vectorized Operations.
+    X: Matrix of features (samples, features)
+    y: Target values
+    """
+    # Adding a column of ones for the intercept (bias term)
+    X = np.c_[np.ones(X.shape[0]), X]
+
+    # Initializing weights with zeros
+    weights = np.zeros(X.shape[1])
+    n = len(y)
+
+    for _ in range(epochs):
+        # Predicting values
+        y_pred = np.dot(X, weights)
+
+        # Calculating Gradients
+        error = y_pred - y
+        gradient = (2/n) * np.dot(X.T, error)
+
+        # Updating weights (Gradient Descent)
+        weights -= lr * gradient
+
+    return weights
+
+# Example Use: Predicting house price based on (Area, Rooms)
+# X = np.array([[1500, 3], [2000, 4], [1200, 2]])
+# y = np.array([300000, 400000, 240000])