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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")
from sklearn.impute import SimpleImputer

# Perform K-means clustering
kmeans = KMeans(n_clusters=3, random_state=0)

# Impute missing values
imputer = SimpleImputer()
imputed_data = imputer.fit_transform(penguins_df[['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g']])

# Fit K-means clustering on imputed data
kmeans.fit(imputed_data)

# Add cluster labels to the dataframe
penguins_df['cluster'] = kmeans.labels_
# Verify the algorithm quality

# Calculate the silhouette score
from sklearn.metrics import silhouette_score

# Calculate the silhouette score for the clustered data
silhouette_score = silhouette_score(imputed_data, kmeans.labels_)

# Print the silhouette score
print('Silhouette Score:', silhouette_score)

# The silhouette score measures the compactness and separation of the clusters.
# A score close to 1 indicates well-separated clusters,
# while a score close to -1 indicates overlapping clusters.

if silhouette_score > 0.5:
    print('The silhouette score is good!')
else:
    print('The silhouette score is not very good.')