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..9a29406d 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
@@ -11,17 +11,25 @@
             // 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, {
-                
-                // 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
+            const convertedTrainingData = trainingData.map(({xs, ys}) => {
+                return {
+                    xs: Object.values(xs),
+                    ys: Object.values(ys)
+                };
+            }).batch(32);
                   
             const testingUrl = '/data/wdbc-test.csv';
             
@@ -30,9 +38,11 @@
             // HINT: Remember that you are trying to build a classifier that 
             // can predict from the data whether the diagnosis is malignant or benign.
             const testingData = tf.data.csv(testingUrl, {
-                
-                // YOUR CODE HERE
-                
+                columnConfigs: {
+                    'diagnosis': {
+                        isLabel: true
+                    }
+                }
             });
             
             // Convert the testing data into arrays in the space below.
@@ -40,13 +50,17 @@
             // 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);
                   
-
             // 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 = 30;
             
                 
             // In the space below create a neural network that predicts 1 if the diagnosis is malignant
@@ -58,16 +72,29 @@
             // 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'
+            }));         
             
             
             // Compile the model using the binaryCrossentropy loss, 
             // the rmsprop optimizer, and accuracy for your metrics. 
-            model.compile(// YOUR CODE HERE);
-            
-            
+            model.compile({
+            loss: 'binaryCrossentropy',
+            optimizer: 'rmsprop',
+            metrics: ['accuracy']
+            });
+              
             await model.fitDataset(convertedTrainingData, 
                              {epochs:100,
                               validationData: convertedTestingData,
@@ -82,4 +109,4 @@
     </script>
 <body>
 </body>
-</html>
\ No newline at end of file
+</html>