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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()
# Convert 'MALE' to 0 and 'FEMALE' to 1 in the 'sex' column
penguins_df['sex'] = penguins_df['sex'].map({'MALE': 0, 'FEMALE': 1})
# Display the first few rows to verify the changes
penguins_df.head()
model = KMeans(n_clusters=6)
model.fit(penguins_df)
# Add the cluster labels to the dataframe
penguins_df['cluster'] = model.labels_
# Plotting the clusters
plt.figure(figsize=(10, 6))
# Scatter plot for each cluster
for cluster in range(6):
clustered_data = penguins_df[penguins_df['cluster'] == cluster]
plt.scatter(clustered_data['flipper_length_mm'], clustered_data['body_mass_g'], label=f'Cluster {cluster}')
# Adding labels and title
plt.xlabel('flipper length')
plt.ylabel('body mass')
plt.title('Penguin Clusters')
plt.legend()
plt.show()
# Add the cluster labels to the dataframe
# penguins_df['cluster'] = model.labels_
# Calculate the average values for each cluster
stat_penguins = penguins_df.groupby('cluster').mean()
# Display the resulting dataframe
stat_penguins = stat_penguins.drop(columns=['sex'])
stat_penguins