diff --git a/machine_learning/ridge_regression/ridge_regression.py b/machine_learning/ridge_regression/ridge_regression.py
index b0908f9ef..1c2c13fa9 100644
--- a/machine_learning/ridge_regression/ridge_regression.py
+++ b/machine_learning/ridge_regression/ridge_regression.py
@@ -15,68 +15,68 @@ class RidgeRegression:
         self.theta: np.ndarray = None
 
     def feature_scaling(
-        self, x: np.ndarray
+        self, features: np.ndarray
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        mean = np.mean(x, axis=0)
-        std = np.std(x, axis=0)
+        mean = np.mean(features, axis=0)
+        std = np.std(features, axis=0)
 
         # avoid division by zero for constant features (std = 0)
         std[std == 0] = 1  # set std=1 for constant features to avoid NaN
 
-        x_scaled = (x - mean) / std
-        return x_scaled, mean, std
+        features_scaled = (features - mean) / std
+        return features_scaled, mean, std
 
-    def fit(self, x: np.ndarray, y: np.ndarray) -> None:
-        x_scaled, mean, std = self.feature_scaling(x)
-        m, n = x_scaled.shape
+    def fit(self, features: np.ndarray, target: np.ndarray) -> None:
+        features_scaled, mean, std = self.feature_scaling(features)
+        m, n = features_scaled.shape
         self.theta = np.zeros(n)  # initializing weights to zeros
 
         for _ in range(self.num_iterations):
-            predictions = x_scaled.dot(self.theta)
-            error = predictions - y
+            predictions = features_scaled.dot(self.theta)
+            error = predictions - target
 
             # computing gradient with L2 regularization
             gradient = (
-                x_scaled.T.dot(error) + self.regularization_param * self.theta
+                features_scaled.T.dot(error) + self.regularization_param * self.theta
             ) / m
             self.theta -= self.alpha * gradient  # updating weights
 
-    def predict(self, x: np.ndarray) -> np.ndarray:
-        x_scaled, _, _ = self.feature_scaling(x)
-        return x_scaled.dot(self.theta)
+    def predict(self, features: np.ndarray) -> np.ndarray:
+        features_scaled, _, _ = self.feature_scaling(features)
+        return features_scaled.dot(self.theta)
 
-    def compute_cost(self, x: np.ndarray, y: np.ndarray) -> float:
-        x_scaled, _, _ = self.feature_scaling(x)
-        m = len(y)
+    def compute_cost(self, features: np.ndarray, target: np.ndarray) -> float:
+        features_scaled, _, _ = self.feature_scaling(features)
+        m = len(target)
 
-        predictions = x_scaled.dot(self.theta)
-        cost = (1 / (2 * m)) * np.sum((predictions - y) ** 2) + (
+        predictions = features_scaled.dot(self.theta)
+        cost = (1 / (2 * m)) * np.sum((predictions - target) ** 2) + (
             self.regularization_param / (2 * m)
         ) * np.sum(self.theta**2)
         return cost
 
-    def mean_absolute_error(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:
-        return np.mean(np.abs(y_true - y_pred))
+    def mean_absolute_error(self, target: np.ndarray, predictions: np.ndarray) -> float:
+        return np.mean(np.abs(target - predictions))
 
 
 # Example usage
 if __name__ == "__main__":
     data = pd.read_csv("ADRvsRating.csv")
-    x = data[["Rating"]].to_numpy()
-    y = data["ADR"].to_numpy()
-    y = (y - np.mean(y)) / np.std(y)
+    features_matrix = data[["Rating"]].to_numpy()
+    target = data["ADR"].to_numpy()
+    target = (target - np.mean(target)) / np.std(target)
 
     # added bias term to the feature matrix
-    x = np.c_[np.ones(x.shape[0]), x]
+    x = np.c_[np.ones(features_matrix.shape[0]), features_matrix]
 
     # initialize and train the ridge regression model
     model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)
-    model.fit(x, y)
+    model.fit(features_matrix, target)
 
     # predictions
-    predictions = model.predict(x)
+    predictions = model.predict(features_matrix)
 
     # results
     print("Optimized Weights:", model.theta)
-    print("Cost:", model.compute_cost(x, y))
-    print("Mean Absolute Error:", model.mean_absolute_error(y, predictions))
+    print("Cost:", model.compute_cost(features_matrix, target))
+    print("Mean Absolute Error:", model.mean_absolute_error(target, predictions))
diff --git a/machine_learning/ridge_regression/test_ridge_regression.py b/machine_learning/ridge_regression/test_ridge_regression.py
index a113fc865..6bf6d6024 100644
--- a/machine_learning/ridge_regression/test_ridge_regression.py
+++ b/machine_learning/ridge_regression/test_ridge_regression.py
@@ -23,9 +23,9 @@ def test_feature_scaling():
        Tests the feature_scaling function of RidgeRegression.
     --------
        >>> model = RidgeRegression()
-       >>> X = np.array([[1, 2], [2, 3], [3, 4]])
-       >>> X_scaled, mean, std = model.feature_scaling(X)
-       >>> np.round(X_scaled, 2)
+       >>> features = np.array([[1, 2], [2, 3], [3, 4]])
+       >>> features_scaled, mean, std = model.feature_scaling(features)
+       >>> np.round(features_scaled, 2)
        array([[-1.22, -1.22],
               [ 0.  ,  0.  ],
               [ 1.22,  1.22]])
@@ -43,14 +43,14 @@ def test_fit():
     >>> model = RidgeRegression(alpha=0.01,
     ...                          regularization_param=0.1,
     ...                          num_iterations=1000)
-    >>> X = np.array([[1], [2], [3]])
-    >>> y = np.array([2, 3, 4])
+    >>> features = np.array([[1], [2], [3]])
+    >>> target = np.array([2, 3, 4])
 
     # Adding a bias term
-    >>> X = np.c_[np.ones(X.shape[0]), X]
+    >>> features = np.c_[np.ones(features.shape[0]), features]
 
     # Fit the model
-    >>> model.fit(X, y)
+    >>> model.fit(features, target)
 
     # Check if the weights have been updated
     >>> np.round(model.theta, decimals=2)
@@ -65,17 +65,17 @@ def test_predict():
     >>> model = RidgeRegression(alpha=0.01,
     ...                          regularization_param=0.1,
     ...                          num_iterations=1000)
-    >>> X = np.array([[1], [2], [3]])
-    >>> y = np.array([2, 3, 4])
+    >>> features = np.array([[1], [2], [3]])
+    >>> target = np.array([2, 3, 4])
 
     # Adding a bias term
-    >>> X = np.c_[np.ones(X.shape[0]), X]
+    >>> features = np.c_[np.ones(features.shape[0]), features]
 
     # Fit the model
-    >>> model.fit(X, y)
+    >>> model.fit(features, target)
 
     # Predict with the model
-    >>> predictions = model.predict(X)
+    >>> predictions = model.predict(features)
     >>> np.round(predictions, decimals=2)
     array([-0.97,  0.  ,  0.97])
     """
@@ -86,9 +86,9 @@ def test_mean_absolute_error():
     Tests the mean_absolute_error function of RidgeRegression
     --------
     >>> model = RidgeRegression()
-    >>> y_true = np.array([2, 3, 4])
-    >>> y_pred = np.array([2.1, 3.0, 3.9])
-    >>> mae = model.mean_absolute_error(y_true, y_pred)
+    >>> target = np.array([2, 3, 4])
+    >>> predictions = np.array([2.1, 3.0, 3.9])
+    >>> mae = model.mean_absolute_error(target, predictions)
     >>> float(np.round(mae, 2))
     0.07
     """