[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

machine_learning/linear_regression.py

@@ -1,6 +1,7 @@
 import numpy as np
 import requests
 
+
 def collect_dataset():
     """Collect dataset of CSGO (ADR vs Rating of a Player)"""
     response = requests.get(
@@ -10,35 +11,40 @@ def collect_dataset():
     data = np.loadtxt(response.text.splitlines()[1:], delimiter=",")  # Skip the header
     return data
 
+
 def normalize_features(data):
     """Normalize feature values to have mean 0 and variance 1"""
     means = np.mean(data[:, :-1], axis=0)
     stds = np.std(data[:, :-1], axis=0)
     data[:, :-1] = (data[:, :-1] - means) / stds
     return data
 
+
 def run_gradient_descent(data_x, data_y, alpha=0.01, iterations=1000, batch_size=32):
     """Run gradient descent with mini-batch optimization"""
     len_data, no_features = data_x.shape
     theta = np.zeros(no_features)
 
     for i in range(iterations):
-        indices = np.random.choice(len_data, batch_size, replace=False)  # Randomly sample indices
+        indices = np.random.choice(
+            len_data, batch_size, replace=False
+        )  # Randomly sample indices
         x_batch = data_x[indices]
         y_batch = data_y[indices]
 
         predictions = x_batch @ theta  # Vectorized predictions
         errors = predictions - y_batch
-        
+
         gradient = (1 / batch_size) * (x_batch.T @ errors)  # Vectorized gradient
         theta -= alpha * gradient  # Update theta
-        
+
         if i % 100 == 0:  # Print error every 100 iterations
-            error = np.mean(errors ** 2)  # Mean Squared Error
+            error = np.mean(errors**2)  # Mean Squared Error
             print(f"Iteration {i}: MSE = {error:.5f}")
 
     return theta
 
+
 def main():
     """Driver function"""
     data = collect_dataset()
@@ -52,5 +58,6 @@ def main():
     print("Resultant Feature vector : ")
     print(theta)
 
+
 if __name__ == "__main__":
     main()