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..c6109fec 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
@@ -1,54 +1,61 @@
+<!DOCTYPE html>
 <html>
-<head></head>
+<head>
+    <title>Diagnosis Classifier</title>
     <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
-    <script lang="js">
-        
+    <script lang="javascript">
         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.
             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 [Object.values(xs), ys.diagnosis === 'M' ? 1 : 0];
+            });
+
             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.
             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
-                  
+            const convertedTestingData = testingData.map(({xs, ys}) => {
+                return [Object.values(xs), ys.diagnosis === 'M' ? 1 : 0];
+            });
 
             // 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.columnNames()).length - 1;
+
             // 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
@@ -58,16 +65,19 @@
             // 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({units: 32, activation: 'relu', inputShape: [numOfFeatures]}));
+            model.add(tf.layers.dense({units: 16, 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,
@@ -76,10 +86,12 @@
                                       console.log("Epoch: " + epoch + " Loss: " + logs.loss + " Accuracy: " + logs.acc);
                                   }
                               }});
+
             await model.save('downloads://my_model');
         }
         run();
     </script>
+</head>
 <body>
 </body>
-</html>
\ No newline at end of file
+</html>
