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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()# Train models with different clusters
scaler = StandardScaler()
X = scaler.fit_transform(penguins_df.drop('sex', axis=1))
models_inertia = []
for i in range(1, 10):
model = KMeans(n_clusters=i)
model.fit(X)
models_inertia.append(model.inertia_)# Plot Inertia vs # Clusters
g = plt.plot(range(1, 10), models_inertia)
g = plt.axvline(x=3, color='g', linestyle='--')
plt.show() # Three clusters is reasonable and it matches the picture at the beginning# Training the model and getting the stats per cluster
model = KMeans(n_clusters=3)
penguins_df['cluster'] = model.fit_predict(X)
stat_penguins = penguins_df.groupby('cluster').mean()
stat_penguins