diff --git a/index.htm b/index.htm
index c64cee9..389e2dd 100644
--- a/index.htm
+++ b/index.htm
@@ -37,6 +37,11 @@ <h2>Linear regression</h2>
   <p>
     <input type="button" class="btn btn-success" value="list of features" onclick="document.editor.textbox.value+='\nCATEGORICAL_COLUMNS = [\'sex\', \'n_siblings_spouses\',\n    \'parch\', \'class\', \'deck\',\n    \'embark_town\', \'alone\']\n\nNUMERIC_COLUMNS = [\'age\', \'fare\']\n\nfeature_columns = []\nfor feature_name in CATEGORICAL_COLUMNS:\n    vocabulary = dftrain[feature_name].unique()  # gets a list of all unique values from given feature column\n    feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))\n\nfor feature_name in NUMERIC_COLUMNS:\n    feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))\n'" />
     <input type="button" class="btn btn-success" value="input function" onclick="document.editor.textbox.value+='\ndef make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):\n    def input_function():\n        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))\n        if shuffle:\n            ds = ds.shuffle(1000)\n        ds = ds.batch(batch_size).repeat(num_epochs)\n        return ds\n    return input_function\n\ntrain_input_fn = make_input_fn(dftrain, y_train)\neval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False)\n'" />
+    <input type="button" class="btn btn-success" value="linear estimator" onclick="document.editor.textbox.value+='\nlinear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)'" />
+  </p>
+
+  <p>
+    <input type="button" class="btn btn-success" value="train model" onclick="document.editor.textbox.value+='\nlinear_est.train(train_input_fn)\nresult = linear_est.evaluate(eval_input_fn)\nprint(result[\'accuracy\'])\n'" />
   </p>
 
 </div>