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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! 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.

ColumnDescription
culmen_length_mmculmen length (mm)
culmen_depth_mmculmen depth (mm)
flipper_length_mmflipper length (mm)
body_mass_gbody mass (g)
sexpenguin 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 dummy variables for "sex" column using get dummies method
penguins_encoded = pd.get_dummies(penguins_df, columns=["sex"], drop_first=True)

# Standardize the encoded dataset
scaler = StandardScaler()
penguins_scaled = scaler.fit_transform(penguins_encoded)

# Perform Elbow analysis to determine the optimal number of clusters
inertia = []
for k in range(1, 10):
    kmeans = KMeans(n_clusters=k, random_state=42)
    kmeans.fit(penguins_scaled)
    inertia.append(kmeans.inertia_)

# Visualize the inertia
plt.plot(range(1, 10), inertia)
plt.xlabel('Number of Clusters')
plt.ylabel('Inertia')
plt.title('Elbow Method For Optimal k')
plt.grid(True)
plt.show()
# Based on the Elbow analysis, determine the optimal number of clusters
optimal_k = 4

# Perform KMeans clustering with the optimal number of clusters
kmeans_final = KMeans(n_clusters=optimal_k, random_state=42)
kmeans_final.fit(penguins_scaled)

# Create a DataFrame to store cluster characteristics
numeric_columns = penguins_df.drop(columns="sex")
numeric_columns["label"] = kmeans_final.labels_

# Calculate the mean of numeric columns for each cluster
stat_penguins = numeric_columns.groupby('label').mean()

# Print the final characteristic table
print(stat_penguins)