diff --git a/01_the_machine_learning_landscape.ipynb b/01_the_machine_learning_landscape.ipynb
index 510909a..9ef739f 100644
--- a/01_the_machine_learning_landscape.ipynb
+++ b/01_the_machine_learning_landscape.ipynb
@@ -750,57 +750,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "backup = oecd_bli, gdp_per_capita\n",
-    "\n",
-    "def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
-    "    return sample_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Replace this linear model:\n",
-    "import sklearn.linear_model\n",
-    "model = sklearn.linear_model.LinearRegression()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# with this k-neighbors regression model:\n",
-    "import sklearn.neighbors\n",
-    "model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = np.c_[country_stats[\"GDP per capita\"]]\n",
-    "y = np.c_[country_stats[\"Life satisfaction\"]]\n",
-    "\n",
-    "# Train the model\n",
-    "model.fit(X, y)\n",
-    "\n",
-    "# Make a prediction for Cyprus\n",
-    "X_new = np.array([[22587.0]])  # Cyprus' GDP per capita\n",
-    "print(model.predict(X_new)) # outputs [[ 5.76666667]]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,