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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()penguins_df_dummies=pd.get_dummies(penguins_df,drop_first=True)
scaler=StandardScaler()
penguins_df_dummies_scaled=scaler.fit_transform(penguins_df_dummies)
print(penguins_df_dummies_scaled.shape)ks=range(1,6)
inertias=[]
for k in ks :
model=KMeans(n_clusters=k)
model.fit(penguins_df_dummies_scaled)
inertias.append(model.inertia_)
plt.plot(ks,inertias)
plt.xlabel('number of clusters, k')
plt.ylabel('inertia')
plt.xticks(ks)
plt.show()model=KMeans(n_clusters=3)
labels=model.fit_predict(penguins_df_dummies_scaled)
print(labels)x=penguins_df_dummies_scaled[:,0]
y=penguins_df_dummies_scaled[:,1]
plt.scatter(x,y,c=labels,alpha=0.5)
centroid=model.cluster_centers_
centroid_x=centroid[:,0]
centroid_y=centroid[:,1]
plt.scatter(centroid_x,centroid_y)
plt.show()penguins_df.insert(5,'clusters',labels)stat_penguins=penguins_df.groupby('clusters').mean()
stat_penguins.head()