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()# create dummies
penguins_df_dummies = pd.get_dummies(penguins_df, drop_first=True)
# standardizing data
scaler = StandardScaler()
penguins_scaled = scaler.fit_transform(penguins_df_dummies)
penguins_scaled_df = pd.DataFrame(penguins_scaled, columns=penguins_df_dummies.columns)# optimal clusters
inertia = []
for i in range(1, 10):
kmeans = KMeans(n_clusters=i, random_state=42)
kmeans.fit(penguins_scaled_df)
inertia.append(kmeans.inertia_)
plt.plot(range(1, 10), inertia, marker="o")
plt.xlabel("Number of Clusters")
plt.ylabel("Inertia")
plt.title("Inertia vs No. of Clusters")
plt.show()# run kmeans
kmeans = KMeans(n_clusters=4, random_state=42)
kmeans.fit(penguins_scaled_df)
penguins_df["label"] = kmeans.labels_# visualize
plt.scatter(penguins_df["body_mass_g"], penguins_df["culmen_length_mm"], c=kmeans.labels_, cmap="viridis")
plt.xlabel("Cluster")
plt.ylabel("Culmen length, mm")
plt.title("K-means clustering (K=4)")
plt.show()# stat analysis
stat_penguins = penguins_df.groupby("label")[["culmen_length_mm", "culmen_depth_mm","flipper_length_mm", "body_mass_g"]].agg("mean")
stat_penguins