Skip to content
Project: Clustering Antarctic Penguin Species
  • AI Chat
  • Code
  • Report

    • Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means clustering

    Alt text 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!

    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.

    • 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 three species that are native to the region: Adelie, Chinstrap, and Gentoo, so 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.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Loading and examining the dataset
penguins_df = pd.read_csv("data/penguins.csv")
    # Explore the data
print(penguins_df.head())
print(penguins_df.info())

# Drop null values
penguins_df = penguins_df.dropna()

# Identify outliers using boxplot
penguins_df.boxplot()
plt.show()
    # Remove outliers (values outside the whiskers)
penguins_df[penguins_df['flipper_length_mm']>4000]
penguins_df[penguins_df['flipper_length_mm']<0]
penguins_clean = penguins_df.drop([9,14])

# Create dummy variables
df = pd.get_dummies(penguins_clean).drop('sex_.',axis=1)

# Standardize the data
scaler = StandardScaler()
penguins_preprocessed = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)

# Perform PCA without specifying the number of components
pca = PCA()
penguins_PCA = pca.fit(penguins_preprocessed)

# Determine the number of components with more than 10% explained variance ratio
n_components = sum(penguins_PCA.explained_variance_ratio_ > 0.1)
    # Run PCA again with the optimal number of components
pca = PCA(n_components=n_components)
penguins_PCA = pca.fit_transform(penguins_preprocessed)

# Perform Elbow analysis
inertia = []
for k in range(1, 10):
    kmeans = KMeans(n_clusters=k, random_state=42).fit(penguins_PCA)
    inertia.append(kmeans.inertia_)

# Visualize the inertia values
plt.plot(range(1, 10), inertia)
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
plt.show()
    # Determine the optimal number of clusters (where the inertia begins to decrease more slowly)
n_clusters = 4

# Run k-means clustering algorithm with the optimal number of clusters
kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(penguins_PCA)

# Visualize the defined clusters
plt.scatter(penguins_PCA[:, 0], penguins_PCA[:, 1], c=kmeans.labels_)
plt.show()

# Add a new column named 'label' to the penguins_clean dataset
penguins_clean['label'] = kmeans.labels_

# Create a list containing the names of the numeric columns
numeric_columns = ['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm','label']

# Create a final characteristic DataFrame
stat_penguins = penguins_clean.groupby('label')[numeric_columns].mean()
    stat_penguins