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..73b01ef0 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
@@ -4,16 +4,18 @@
     <script lang="js">
         
         async function run(){
-            const trainingUrl = '/data/wdbc-train.csv';
+            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.
@@ -21,17 +23,22 @@
             // 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(10);
                   
-            const testingUrl = '/data/wdbc-test.csv';
+            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 
+                    }
+                }
                 
             });
             
@@ -40,13 +47,14 @@
             // 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(10);
 
             // 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
@@ -58,15 +66,18 @@
             // 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: 50, activation: 'relu'}));
+            model.add(tf.layers.dense({units: 50, 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({ 
+                optimizer: 'rmsprop',
+                loss: 'binaryCrossentropy',
+                metrics: ['accuracy']
+            });
             
             await model.fitDataset(convertedTrainingData, 
                              {epochs:100,
@@ -82,4 +93,4 @@
     </script>
 <body>
 </body>
-</html>
\ No newline at end of file
+</html>