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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()
#Changing sex column to dummy
df = pd.get_dummies(penguins_df, drop_first=True)
print(df.head())
#transform and cluster dataset
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df)
inertias = []
for i in range(1, 10):
kmeans = KMeans(n_clusters=i, random_state=42).fit(df_scaled)
inertias.append(kmeans.inertia_)
plt.plot(range(1, 10), inertias)
plt.xlabel('Number of Clusters')
plt.ylabel('Inertia')
plt.title('Elbow Method For Optimal k')
plt.show()
new_df = pd.DataFrame(df_scaled, columns=df.columns)
kmeans = KMeans(n_clusters=4)
kmeans.fit(new_df)
labels = kmeans.labels_
df['label'] = labels
plt.scatter(df['label'], df['culmen_length_mm'])
plt.xlabel('Cluster Label')
plt.ylabel('Culmen Length (mm)')
plt.title('Cluster Labels vs Culmen Length')
plt.show()
numeric_columns = ['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g']
stat_penguins = df[numeric_columns + ['label']].groupby('label').mean()
print(stat_penguins)