added ridge regression

machine_learning/ridge_regression/model.py

@@ -1,31 +1,33 @@
 import numpy as np
 import pandas as pd
 
 
 class RidgeRegression:
     def __init__(self, alpha:float=0.001, regularization_param:float=0.1, num_iterations:int=1000) -> None:
         self.alpha:float = alpha
         self.regularization_param:float = regularization_param
         self.num_iterations:int = num_iterations
         self.theta:np.ndarray = None
 
 
     def feature_scaling(self, X:np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
         mean = np.mean(X, axis=0)
         std = np.std(X, 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
 
+
     def fit(self, X:np.ndarray, y:np.ndarray) -> None:
         X_scaled, mean, std = self.feature_scaling(X)
         m, n = X_scaled.shape
         self.theta = np.zeros(n)  # initializing weights to zeros
 
+
         for i in range(self.num_iterations):
             predictions = X_scaled.dot(self.theta)
             error = predictions - y
 
@@ -35,12 +37,14 @@
             ) / 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 compute_cost(self, X:np.ndarray, y:np.ndarray) -> float:
-        X_scaled, _, _ = self.feature_scaling(X)  
+        X_scaled, _, _ = self.feature_scaling(X)
         m = len(y)
 
         predictions = X_scaled.dot(self.theta)
@@ -49,6 +53,7 @@
         ) * 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))