diff --git a/07_linear_regression.py b/07_linear_regression.py
index c39db2bb..594067bf 100644
--- a/07_linear_regression.py
+++ b/07_linear_regression.py
@@ -2,50 +2,72 @@
 import torch.nn as nn
 import numpy as np
 from sklearn import datasets
-import matplotlib.pyplot as plt
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
 
-# 0) Prepare data
-X_numpy, y_numpy = datasets.make_regression(n_samples=100, n_features=1, noise=20, random_state=4)
-
-# cast to float Tensor
-X = torch.from_numpy(X_numpy.astype(np.float32))
-y = torch.from_numpy(y_numpy.astype(np.float32))
-y = y.view(y.shape[0], 1)
+# 2025/09/16
+# 0) prepare data
+bc = datasets.load_breast_cancer()
+X, y = bc.data, bc.target
 
 n_samples, n_features = X.shape
 
-# 1) Model
-# Linear model f = wx + b
-input_size = n_features
-output_size = 1
-model = nn.Linear(input_size, output_size)
+X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=0.2, random_state=1234)
+
+# scale
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+
+X_train = torch.from_numpy(X_train.astype(np.float32))
+X_test = torch.from_numpy(X_test.astype(np.float32))
+y_train = torch.from_numpy(y_train.astype(np.float32))
+y_test = torch.from_numpy(y_test.astype(np.float32))
+
+y_train = y_train.view(y_train.shape[0],1)
+y_test = y_test.view(y_test.shape[0],1)
+
+# 1)model
+# f= = wx + b,sigmoid at the end
+class LogisticRegression(nn.Module):
+  def __init__(self,n_input_features):
+    super(LogisticRegression, self).__init__()
+    self.linear = nn.Linear(n_input_features,1)
+
+  def forward(self,x):
+    y_predicted =torch.sigmoid(self.linear(x))
+    return y_predicted
+
+model = LogisticRegression(n_features)
 
 # 2) Loss and optimizer
 learning_rate = 0.01
+criterion = nn.BCELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
 
-criterion = nn.MSELoss()
-optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
-
-# 3) Training loop
+# 3)training loop
 num_epochs = 100
 for epoch in range(num_epochs):
-    # Forward pass and loss
-    y_predicted = model(X)
-    loss = criterion(y_predicted, y)
-    
-    # Backward pass and update
-    loss.backward()
-    optimizer.step()
-
-    # zero grad before new step
-    optimizer.zero_grad()
-
-    if (epoch+1) % 10 == 0:
+  # forward pass and loss
+  y_predicted = model(X_train)
+  loss = criterion(y_predicted, y_train)
+
+  # backward pass
+  loss.backward()
+
+  # updates
+  optimizer.step()
+
+  # zero gradients
+  optimizer.zero_grad()
+
+  if (epoch+1) % 10 == 0:
         print(f'epoch: {epoch+1}, loss = {loss.item():.4f}')
 
-# Plot
-predicted = model(X).detach().numpy()
+with torch.no_grad():
+  y_predicted = model(X_test)
+  y_predicted_cls = y_predicted.round()
+  acc = y_predicted_cls.eq(y_test).sum() / float(y_test.shape[0])
+  print(f'accuracy ={acc:.4f}')
+   
 
-plt.plot(X_numpy, y_numpy, 'ro')
-plt.plot(X_numpy, predicted, 'b')
-plt.show()