diff --git a/index.htm b/index.htm
index ab57431..86b13d4 100644
--- a/index.htm
+++ b/index.htm
@@ -24,7 +24,7 @@ <h2>Linear regression</h2>
   <p>
     <input type="button" class="btn btn-module" value="modules" onclick="document.editor.textbox.value+='\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom IPython.display import clear_output\nfrom six.moves import urllib\nimport tensorflow.compat.v2.feature_column as fc\nimport tensorflow as tf\n'" />
     <input type="button" class="btn btn-success" value="sample dataset" onclick="document.editor.textbox.value+='\nx = [1, 2, 3, 4, 5]\ny = [1, 3, 8, 10, 14]\nplt.plot(x, y, \'ro\')\nplt.axis([0, 6, 0, 15])\nx_unique = np.unique(x)\npoly_func = np.poly1d(np.polyfit(x, y, 1))\nplt.plot(x_unique, poly_func(x_unique))\nplt.show()\n'" />
-    <input type="button" class="btn btn-success" value="load dataset" onclick="document.editor.textbox.value+='\ndftrain = pd.read_csv(\'https://github.com/Supravisor/TensorFlow_2/blob/main/datasets/train.csv\') # training data\ndfeval = pd.read_csv(\'https://github.com/Supravisor/TensorFlow_2/blob/main/datasets/eval.csv\') # testing data\ny_train = dftrain.pop(\'survived\')\ny_eval = dfeval.pop(\'survived\')\n'" />
+    <input type="button" class="btn btn-success" value="load dataset" onclick="document.editor.textbox.value+='\ndftrain = pd.read_csv(\'https://raw.githubusercontent.com/Supravisor/TensorFlow_2/refs/heads/project/datasets/train.csv\') # training data\ndfeval = pd.read_csv(\'https://raw.githubusercontent.com/Supravisor/TensorFlow_2/refs/heads/project/datasets/eval.csv\') # testing data\ny_train = dftrain.pop(\'survived\')\ny_eval = dfeval.pop(\'survived\')\n'" />
   </p>
 
   <p>
@@ -35,7 +35,7 @@ <h2>Linear regression</h2>
   </p>
 
   <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'" />
+    <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'" />
   </p>
 
 </div>