diff --git a/C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html b/C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html
index 023c6143..96125d4d 100755
--- a/C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html
+++ b/C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html
@@ -6,80 +6,68 @@
         async function run(){
             const trainingUrl = '/data/wdbc-train.csv';
             
-            // Take a look at the 'wdbc-train.csv' file and specify the column
-            // that should be treated as the label in the space below.
-            // HINT: Remember that you are trying to build a classifier that 
-            // can predict from the data whether the diagnosis is malignant or benign.
+            // Specify the column that should be treated as the label
             const trainingData = tf.data.csv(trainingUrl, {
-                
-                // YOUR CODE HERE
-                
+                columnConfigs: {
+                    diagnosis: { isLabel: true }
+                }
             });
             
-            // Convert the training data into arrays in the space below.
-            // Note: In this case, the labels are integers, not strings.
-            // Therefore, there is no need to convert string labels into
-            // a one-hot encoded array of label values like we did in the
-            // Iris dataset example. 
-            const convertedTrainingData = // YOUR CODE HERE
-                  
+            // Convert the training data into arrays
+            const convertedTrainingData = trainingData
+                .map(({ xs, ys }) => {
+                    return { xs: Object.values(xs), ys: Object.values(ys)[0] };
+                })
+                .batch(32);
+
             const testingUrl = '/data/wdbc-test.csv';
             
-            // Take a look at the 'wdbc-test.csv' file and specify the column
-            // that should be treated as the label in the space below..
-            // HINT: Remember that you are trying to build a classifier that 
-            // can predict from the data whether the diagnosis is malignant or benign.
+            // Specify the column that should be treated as the label for testing data
             const testingData = tf.data.csv(testingUrl, {
-                
-                // YOUR CODE HERE
-                
+                columnConfigs: {
+                    diagnosis: { isLabel: true }
+                }
             });
             
-            // Convert the testing data into arrays in the space below.
-            // Note: In this case, the labels are integers, not strings.
-            // Therefore, there is no need to convert string labels into
-            // a one-hot encoded array of label values like we did in the
-            // Iris dataset example. 
-            const convertedTestingData = // YOUR CODE HERE
-                  
+            // Convert the testing data into arrays
+            const convertedTestingData = testingData
+                .map(({ xs, ys }) => {
+                    return { xs: Object.values(xs), ys: Object.values(ys)[0] };
+                })
+                .batch(32);
 
-            // Specify the number of features in the space below.
-            // HINT: You can get the number of features from the number of columns
-            // and the number of labels in the training data. 
-            const numOfFeatures = // YOUR CODE HERE
-            
-                
-            // In the space below create a neural network that predicts 1 if the diagnosis is malignant
-            // and 0 if the diagnosis is benign. Your neural network should only use dense
-            // layers and the output layer should only have a single output unit with a
-            // sigmoid activation function. You are free to use as many hidden layers and
-            // neurons as you like.  
-            // HINT: Make sure your input layer has the correct input shape. We also suggest
-            // using ReLu activation functions where applicable. For this dataset only a few
-            // hidden layers should be enough to get a high accuracy.  
+            // Specify the number of features (columns minus the label)
+            const numOfFeatures = 30;
+
+            // Create a neural network
             const model = tf.sequential();
-            
-            // YOUR CODE HERE
+            model.add(tf.layers.dense({ inputShape: [numOfFeatures], units: 16, activation: 'relu' }));
+            model.add(tf.layers.dense({ units: 8, activation: 'relu' }));
+            model.add(tf.layers.dense({ units: 4, activation: 'relu' }));
+            model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }));
 
+            // Compile the model
+            model.compile({
+                loss: 'binaryCrossentropy',
+                optimizer: 'rmsprop',
+                metrics: ['accuracy']
+            });
             
-            
-            // Compile the model using the binaryCrossentropy loss, 
-            // the rmsprop optimizer, and accuracy for your metrics. 
-            model.compile(// YOUR CODE HERE);
-            
-            
+            // Train the model
             await model.fitDataset(convertedTrainingData, 
-                             {epochs:100,
-                              validationData: convertedTestingData,
-                              callbacks:{
-                                  onEpochEnd: async(epoch, logs) =>{
-                                      console.log("Epoch: " + epoch + " Loss: " + logs.loss + " Accuracy: " + logs.acc);
-                                  }
-                              }});
+                {epochs: 100,
+                 validationData: convertedTestingData,
+                 callbacks:{
+                     onEpochEnd: async(epoch, logs) =>{
+                         console.log("Epoch: " + epoch + " Loss: " + logs.loss + " Accuracy: " + logs.acc);
+                     }
+                 }});
+            
+            // Save the model
             await model.save('downloads://my_model');
         }
         run();
     </script>
 <body>
 </body>
-</html>
\ No newline at end of file
+</html>