Finished the implementation of the python code.

mini-batch-sgd-linear-regression-parkinsons.py

@@ -3,8 +3,8 @@ import pandas as pd
 
 class LinearRegression:
     '''
-        Constructor for the Linear Regression with mini‑batch stochastic gradient descent. It uses learning rate,
-        iteration number, batch size, bias and verbose. It also initializes the weight, mean and std.
+        Constructor for the linear regression with mini‑batch stochastic gradient descent. It uses learning rate,
+        iteration number, batch size, bias and verbose. It also initializes the weight, mean and standard deviation.
     '''
     def __init__(self, lr, n_iter, batch_size, add_bias, verbose):
         self.lr = lr  # learning rate
@@ -90,7 +90,7 @@ class LinearRegression:
 
     def predict(self, x: pd.DataFrame) -> pd.Series:
         '''
-            Predict method makes X prediction by multiplying X and weight vectors.
+            Predict method is used to test trained data to do Y prediction by multiplying X and weight vectors.
         '''
         if self.w is None:  # if weight is empty, throw error
             raise RuntimeError("Model is not fitted yet. Call `fit` first.")
@@ -101,7 +101,7 @@ class LinearRegression:
     def score(self, x: pd.DataFrame, y: pd.Series) -> float:
         '''
             This method is used to calculate coefficient of determination to assess the goodness
-            of fit from a regression model
+            of fit from the linear regression model
         '''
         y_pred = self.predict(x)  # predicts Y value with X predict method.
         y = pd.Series(y).astype('float64')
@@ -133,7 +133,7 @@ if __name__ == "__main__":
         df[col] = pd.to_numeric(df[col], errors='coerce') # convert columns to numeric values
 
     df.dropna(inplace=True) # remove null values
-    print(f"Rows remaining after drop of the null values: {len(df)}")
+    print(f"Rows remaining after drop of the null values: {len(df)}\n")
 
     # sanity checks for data validity - realistic parkinson data range estimations
     df = df[(df['age'] >= 18) & (df['age'] <= 95)]
@@ -164,12 +164,9 @@ if __name__ == "__main__":
 
     # evaluation of the model
     print("\nR² on training data:", model.score(x_train, y_train))
-    print("\nR² on testing data:", model.score(x_test, y_test))
+    print("R² on testing data:", model.score(x_test, y_test))
 
     # predict Y values using the trained data
     preds = model.predict(x_test)
-    print("\nFirst 5 predictions:")
-    print(preds.head())
-
-    print("\nWeights:")
-    print(model.w.round(4))
+    print("\nFirst 10 predictions:")
+    print(preds.head(10))