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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
from sklearn.pipeline import make_pipeline# Loading and examining the dataset
penguins_df = pd.read_csv("penguins.csv")
penguins_df.head()penguins_df.info()penguins_df.describe()penguins_df['sex'] = pd.get_dummies(penguins_df['sex'],drop_first=True)inertias = []
for n_cluster in range(3,11):
scaler = StandardScaler()
kmeans = KMeans(n_cluster,random_state=0)
pipeline = make_pipeline(scaler,kmeans)
pipeline.fit(penguins_df)
inertias.append(kmeans.inertia_)
plt.plot(range(3,11),inertias)# scaler = StandardScaler()
# kmeans = KMeans(8,random_state=0)
# pipeline = make_pipeline(scaler,kmeans)
# pipeline.fit(penguins_df)
# kmeans.inertia_scaler = StandardScaler()
kmeans = KMeans(6,random_state=0)
pipeline = make_pipeline(scaler,kmeans)
pipeline.fit(penguins_df)
kmeans.inertia_penguins_df['labels'] = pipeline.predict(penguins_df)stat_penguins = penguins_df.groupby('labels').mean()# stat_penguins = pd.DataFrame(kmeans.cluster_centers_)