Skip to content
Project: Clustering Antarctic Penguin Species
source: @allison_horst https://github.com/allisonhorst/penguins
You have been asked to support a team of researchers who have been collecting data about penguins in Antartica! The data is available in csv-Format as penguins.csv
Origin of this data : Data were collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of the Long Term Ecological Research Network.
The dataset consists of 5 columns.
| Column | Description |
|---|---|
| culmen_length_mm | culmen length (mm) |
| culmen_depth_mm | culmen depth (mm) |
| flipper_length_mm | flipper length (mm) |
| body_mass_g | body mass (g) |
| sex | penguin sex |
Unfortunately, they have not been able to record the species of penguin, but they know that there are at least three species that are native to the region: Adelie, Chinstrap, and Gentoo. Your task is to apply your data science skills to help them identify groups in the dataset!
# Import Required Packages
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Loading and examining the dataset
penguins_df = pd.read_csv("penguins.csv")
penguins_df.head()# Select only numeric columns for clustering
numeric_cols = ['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g']
X = penguins_df[numeric_cols].dropna()
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Choose a reasonable number of clusters (3, as there are 3 penguin species)
kmeans = KMeans(n_clusters=3, random_state=42)
clusters = kmeans.fit_predict(X_scaled)
# Assign cluster labels to the original DataFrame (excluding non-numeric columns)
penguins_clustered = X.copy()
penguins_clustered['cluster'] = clusters
# Calculate the mean of each variable by cluster
stat_penguins = penguins_clustered.groupby('cluster').mean().reset_index()
stat_penguins # Plotting the clusters
plt.figure(figsize=(10, 6))
plt.scatter(
penguins_clustered['culmen_length_mm'],
penguins_clustered['culmen_depth_mm'],
c=penguins_clustered['cluster'],
cmap='viridis',
alpha=0.7
)
plt.xlabel('Culmen Length (mm)')
plt.ylabel('Culmen Depth (mm)')
plt.title('Penguin Clusters by Culmen Measurements')
plt.colorbar(label='Cluster')
plt.show()