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..8f0a2fc6 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
@@ -11,6 +11,11 @@
             // HINT: Remember that you are trying to build a classifier that 
             // can predict from the data whether the diagnosis is malignant or benign.
             const trainingData = tf.data.csv(trainingUrl, {
+                columnConfigs: {
+                    diagnosis: { 
+                        isLabel: true 
+                    } 
+                }  
                 
                 // YOUR CODE HERE
                 
@@ -21,7 +26,12 @@
             // 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
+            const convertedTrainingData = trainingData.map(({ xs, ys }) => {
+                return {
+                    xs: Object.values(xs),
+                    ys: Object.values(ys)
+                };
+            }).batch(32); // YOUR CODE HERE
                   
             const testingUrl = '/data/wdbc-test.csv';
             
@@ -30,7 +40,9 @@
             // HINT: Remember that you are trying to build a classifier that 
             // can predict from the data whether the diagnosis is malignant or benign.
             const testingData = tf.data.csv(testingUrl, {
-                
+                columnConfigs: {
+                    diagnosis: { isLabel: true }
+                }
                 // YOUR CODE HERE
                 
             });
@@ -40,13 +52,18 @@
             // 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
+            const convertedTestingData = testingData.map(({ xs, ys }) => {
+                return {
+                    xs: Object.values(xs),
+                    ys: Object.values(ys)
+                };
+            }).batch(32); // YOUR CODE HERE
                   
 
             // 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
+            const numOfFeatures = (await trainingData.take(1).toArray())[0].xs.length; // YOUR CODE HERE
             
                 
             // In the space below create a neural network that predicts 1 if the diagnosis is malignant
@@ -58,28 +75,33 @@
             // using ReLu activation functions where applicable. For this dataset only a few
             // hidden layers should be enough to get a high accuracy.  
             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: 1, activation: 'sigmoid' }));
 
-            
-            
+            // YOUR CODE HERE
             // Compile the model using the binaryCrossentropy loss, 
             // the rmsprop optimizer, and accuracy for your metrics. 
-            model.compile(// YOUR CODE HERE);
+            model.compile({
+                optimizer: tf.optimizers.rmsprop(),
+                loss: 'binaryCrossentropy',
+                metrics: ['accuracy']
+            });
             
+            await model.fitDataset(convertedTrainingData, {
+                epochs: 100,
+                validationData: convertedTestingData,
+                callbacks: {
+                    onEpochEnd: async(epoch, logs) => {
+                        console.log("Epoch: " + epoch + " Loss: " + logs.loss + " Accuracy: " + logs.acc);
+                    }
+                }
+            });
             
-            await model.fitDataset(convertedTrainingData, 
-                             {epochs:100,
-                              validationData: convertedTestingData,
-                              callbacks:{
-                                  onEpochEnd: async(epoch, logs) =>{
-                                      console.log("Epoch: " + epoch + " Loss: " + logs.loss + " Accuracy: " + logs.acc);
-                                  }
-                              }});
             await model.save('downloads://my_model');
         }
         run();
     </script>
 <body>
 </body>
-</html>
\ No newline at end of file
+</html>