From 7ea2f6625e6cbc1050bc3394ee481e6f8d484d15 Mon Sep 17 00:00:00 2001
From: Ruhanika <ruhanika2006@gmail.com>
Date: Tue, 7 Oct 2025 11:03:45 +0530
Subject: [PATCH 1/2] enhancement

---
 machine_learning/01_linear_regression.py | 54 ++++++++++++++++++++++++
 1 file changed, 54 insertions(+)
 create mode 100644 machine_learning/01_linear_regression.py

diff --git a/machine_learning/01_linear_regression.py b/machine_learning/01_linear_regression.py
new file mode 100644
index 000000000000..f74f649d1054
--- /dev/null
+++ b/machine_learning/01_linear_regression.py
@@ -0,0 +1,54 @@
+import numpy as np
+
+# -------------------- Naive Linear Regression --------------------
+def naive_linear_regression(X, y, learning_rate=0.01, epochs=1000):
+    """
+    Naive Linear Regression using loops.
+    X: input features (2D array)
+    y: target values (column vector)
+    """
+    m, n = X.shape
+    theta = np.zeros((n, 1))  # initialize parameters
+
+    for _ in range(epochs):
+        predictions = []
+        for i in range(m):
+            pred = 0
+            for j in range(n):
+                pred += X[i][j] * theta[j][0]
+            predictions.append([pred])
+        predictions = np.array(predictions)
+        # compute gradient
+        errors = predictions - y
+        for j in range(n):
+            grad = 0
+            for i in range(m):
+                grad += errors[i][0] * X[i][j]
+            theta[j][0] -= learning_rate * grad / m
+    return theta
+
+# -------------------- Vectorized Linear Regression --------------------
+def vectorized_linear_regression(X, y, learning_rate=0.01, epochs=1000):
+    """
+    Fully vectorized Linear Regression using matrix operations.
+    """
+    m, n = X.shape
+    theta = np.zeros((n, 1))
+    for _ in range(epochs):
+        predictions = X.dot(theta)
+        errors = predictions - y
+        gradient = (X.T.dot(errors)) / m
+        theta -= learning_rate * gradient
+    return theta
+
+# -------------------- Test Both Implementations --------------------
+if __name__ == "__main__":
+    # Sample dataset
+    X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
+    y = np.dot(X, np.array([[1],[2]])) + 3  # y = 1*x1 + 2*x2 + 3
+
+    theta_naive = naive_linear_regression(X, y)
+    theta_vec = vectorized_linear_regression(X, y)
+
+    print("Theta naive:\n", theta_naive)
+    print("Theta vectorized:\n", theta_vec)

From cb783fe205d526247d1c8ca46493690c4b515746 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 10 Sep 2026 17:18:02 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 machine_learning/01_linear_regression.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/machine_learning/01_linear_regression.py b/machine_learning/01_linear_regression.py
index f74f649d1054..616c66df5352 100644
--- a/machine_learning/01_linear_regression.py
+++ b/machine_learning/01_linear_regression.py
@@ -1,5 +1,6 @@
 import numpy as np
 
+
 # -------------------- Naive Linear Regression --------------------
 def naive_linear_regression(X, y, learning_rate=0.01, epochs=1000):
     """
@@ -27,6 +28,7 @@ def naive_linear_regression(X, y, learning_rate=0.01, epochs=1000):
             theta[j][0] -= learning_rate * grad / m
     return theta
 
+
 # -------------------- Vectorized Linear Regression --------------------
 def vectorized_linear_regression(X, y, learning_rate=0.01, epochs=1000):
     """
@@ -41,11 +43,12 @@ def vectorized_linear_regression(X, y, learning_rate=0.01, epochs=1000):
         theta -= learning_rate * gradient
     return theta
 
+
 # -------------------- Test Both Implementations --------------------
 if __name__ == "__main__":
     # Sample dataset
     X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
-    y = np.dot(X, np.array([[1],[2]])) + 3  # y = 1*x1 + 2*x2 + 3
+    y = np.dot(X, np.array([[1], [2]])) + 3  # y = 1*x1 + 2*x2 + 3
 
     theta_naive = naive_linear_regression(X, y)
     theta_vec = vectorized_linear_regression(X, y)