diff --git a/machine_learning/ridge_regression/model.py b/machine_learning/ridge_regression/ridge_regression.py
similarity index 100%
rename from machine_learning/ridge_regression/model.py
rename to machine_learning/ridge_regression/ridge_regression.py
diff --git a/machine_learning/ridge_regression/test_ridge_regression.py b/machine_learning/ridge_regression/test_ridge_regression.py
new file mode 100644
index 000000000..03c4218a5
--- /dev/null
+++ b/machine_learning/ridge_regression/test_ridge_regression.py
@@ -0,0 +1,91 @@
+"""
+Doctest for RidgeRegression class
+
+Tests include:
+- feature_scaling
+- fit
+- predict
+- mean_absolute_error
+
+To run these tests, use the following command:
+    python -m doctest test_ridge_regression.py -v
+"""
+
+import numpy as np
+from ridge_regression import RidgeRegression
+
+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)
+    array([[-1.22, -1.22],
+           [ 0.  ,  0.  ],
+           [ 1.22,  1.22]])
+    >>> np.round(mean, 2)
+    array([2., 3.])
+    >>> np.round(std, 2)
+    array([0.82, 0.82])
+    """
+    pass
+
+def test_fit():
+    """
+    Tests the fit function of RidgeRegression
+    --------
+    >>> model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)
+    >>> X = np.array([[1], [2], [3]])
+    >>> y = np.array([2, 3, 4])
+    
+    # Adding a bias term
+    >>> X = np.c_[np.ones(X.shape[0]), X]
+    
+    # Fit the model
+    >>> model.fit(X, y)
+    
+    # Check if the weights have been updated
+    >>> np.round(model.theta, decimals=2)
+    array([0.  , 0.79])
+    """
+    pass
+
+def test_predict():
+    """
+    Tests the predict function of RidgeRegression
+    --------
+    >>> model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)
+    >>> X = np.array([[1], [2], [3]])
+    >>> y = np.array([2, 3, 4])
+    
+    # Adding a bias term
+    >>> X = np.c_[np.ones(X.shape[0]), X]
+    
+    # Fit the model
+    >>> model.fit(X, y)
+    
+    # Predict with the model
+    >>> predictions = model.predict(X)
+    >>> np.round(predictions, decimals=2)
+    array([-0.97,  0.  ,  0.97])
+    """
+    pass
+
+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)
+    >>> float(np.round(mae, 2))
+    0.07
+    """
+    pass
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
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