Scaled features

Author: GraemeMalcolm

04 - Clustering.ipynb

@@ -10,7 +10,7 @@
         "\n",
         "For example, let's take a look at the Palmer Islands penguin dataset, which contains measurements of penguins.\n",
         "\n",
-        "Let's start by examining a dataset that contains observations of multiple classes. We'll use a dataset that contains observations of three different species pf penguin.\n",
+        "Let's start by examining a dataset that contains observations of multiple classes. We'll use a dataset that contains observations of three different species of penguin.\n",
         "\n",
         "> **Citation**: The penguins dataset used in the this exercise is a subset of data collected and made available by [Dr. Kristen\n",
         "Gorman](https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php)\n",
@@ -52,8 +52,13 @@
       },
       "cell_type": "code",
       "source": [
+        "from sklearn.preprocessing import MinMaxScaler\n",
         "from sklearn.decomposition import PCA\n",
         "\n",
+        "# Normalize the numeric features so they're on the same scale\n",
+        "penguin_features[penguins.columns[0:4]] = MinMaxScaler().fit_transform(penguin_features[penguins.columns[0:4]])\n",
+        "\n",
+        "# Get two principal components\n",
         "pca = PCA(n_components=2).fit(penguin_features.values)\n",
         "penguins_2d = pca.transform(penguin_features.values)\n",
         "penguins_2d[0:10]"