diff --git a/01_the_machine_learning_landscape.ipynb b/01_the_machine_learning_landscape.ipynb
index 68a6720..618aff4 100644
--- a/01_the_machine_learning_landscape.ipynb
+++ b/01_the_machine_learning_landscape.ipynb
@@ -645,13 +645,13 @@
     "plt.xlabel(\"GDP per capita (USD)\")\n",
     "plt.ylabel('Life satisfaction')\n",
     "\n",
-    "plt.plot(list(country_stats[\"GDP per capita (USD)\"]),\n",
-    "         list(country_stats[\"Life satisfaction\"]), \"bo\")\n",
-    "plt.plot(list(missing_data[\"GDP per capita (USD)\"]),\n",
-    "         list(missing_data[\"Life satisfaction\"]), \"rs\")\n",
+    "country_stats.plot(ax=plt.gca(), kind='scatter',\n",
+    "                   x=gdppc, y='Life satisfaction')\n",
+    "missing_data.plot(ax=plt.gca(), kind='scatter',\n",
+    "                  x=gdppc, y='Life satisfaction', marker=\"s\", color=\"r\")\n",
     "\n",
     "X = np.linspace(0, 115_000, 1000)\n",
-    "plt.plot(X, t0full + t1full * X, \"r--\", label=\"Linear model on all data\")\n",
+    "plt.plot(X, t0full + t1full * X, \"k-\", label=\"Linear model on all data\")\n",
     "plt.plot(X, t0 + t1*X, \"b:\", label=\"Linear model on partial data\")\n",
     "\n",
     "ridge = linear_model.Ridge(alpha=10**9.5)\n",
@@ -659,11 +659,10 @@
     "ysample = country_stats[[\"Life satisfaction\"]]\n",
     "ridge.fit(Xsample, ysample)\n",
     "t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
-    "plt.plot(X, t0ridge + t1ridge * X, \"b\", label=\"Regularized linear model on partial data\")\n",
+    "plt.plot(X, t0ridge + t1ridge * X, \"b--\", label=\"Regularized linear model on partial data\")\n",
     "\n",
     "plt.legend(loc=\"lower right\")\n",
     "plt.axis([0, 115_000, 0, 10])\n",
-    "plt.xlabel(\"GDP per capita (USD)\")\n",
     "\n",
     "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
     "plt.grid(True)\n",