diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb
index f298b102c..e87b630bd 100644
--- a/doc/pub/week34/ipynb/week34.ipynb
+++ b/doc/pub/week34/ipynb/week34.ipynb
@@ -1559,10 +1559,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 2,
    "id": "bae736cf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Importing various packages\n",
     "import numpy as np\n",
@@ -1570,7 +1581,7 @@
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
     "x = np.random.rand(100,1)\n",
-    "y = 2*x+np.random.randn(100,1)\n",
+    "y = 2*x#+np.random.randn(100,1)\n",
     "linreg = LinearRegression()\n",
     "linreg.fit(x,y)\n",
     "xnew = np.array([[0],[1]])\n",