diff --git a/01_the_machine_learning_landscape.ipynb b/01_the_machine_learning_landscape.ipynb
index 71e6f3b..68a6720 100644
--- a/01_the_machine_learning_landscape.ipynb
+++ b/01_the_machine_learning_landscape.ipynb
@@ -118,7 +118,7 @@
     "import pandas as pd\n",
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
-    "# Load the data\n",
+    "# Load and prepare the data\n",
     "lifesat = pd.read_csv(Path() / \"datasets\" / \"lifesat\" / \"lifesat.csv\")\n",
     "X = lifesat[[\"GDP per capita (USD)\"]].values\n",
     "y = lifesat[[\"Life satisfaction\"]].values\n",
@@ -305,7 +305,7 @@
    "outputs": [],
    "source": [
     "full_country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)\n",
-    "full_country_stats.to_csv(datapath / \"lifesat.csv\")"
+    "full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
    ]
   },
   {
@@ -326,6 +326,7 @@
     "max_gdp = 62_500\n",
     "country_stats = full_country_stats[(full_country_stats[gdppc] >= min_gdp) &\n",
     "                                   (full_country_stats[gdppc] <= max_gdp)]\n",
+    "country_stats.to_csv(datapath / \"lifesat.csv\")\n",
     "country_stats.head()"
    ]
   },
@@ -373,7 +374,7 @@
    "outputs": [],
    "source": [
     "highlighted_countries = country_stats.loc[list(position_text.keys())]\n",
-    "highlighted_countries[[\"Life satisfaction\"]].sort_values(by=\"Life satisfaction\")"
+    "highlighted_countries[[gdppc, \"Life satisfaction\"]].sort_values(by=gdppc)"
    ]
   },
   {