k-Nearest Neighbors is added along with the Linear Regression model for example 1-1

01_the_machine_learning_landscape.ipynb

@@ -170,6 +170,39 @@
     "print(model.predict(X_new)) # outputs [[ 5.96242338]]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Replacing the Linear Regression model with k-Nearest Neighbors (in this example, k = 3) regression in the previous code is as simple as replacing these two\n",
+    "lines:\n",
+    "\n",
+    "import sklearn.linear_model\n",
+    "model = sklearn.linear_model.LinearRegression()\n",
+    "\n",
+    "with these two:\n",
+    "\n",
+    "import sklearn.neighbors\n",
+    "model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select a 3-Nearest Neighbors regression model\n",
+    "import sklearn.neighbors\n",
+    "model1 = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)\n",
+    "\n",
+    "# Train the model\n",
+    "model1.fit(X,y)\n",
+    "\n",
+    "# Make a prediction for Cyprus\n",
+    "print(model1.predict(X_new)) # outputs [[5.76666667]]\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -771,9 +804,8 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+   "display_name": "Python 3.9.4 64-bit",
+   "name": "python39464bite0b9acfa2c9e400a974360d829d6c43a"
   },
   "language_info": {
    "codemirror_mode": {
@@ -785,7 +817,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.4-final"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "22b0ec00cd9e253c751e6d2619fc0bb2d18ed12980de3246690d5be49479dd65"
+   }
   },
   "nav_menu": {},
   "toc": {