Skip to content

Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means clustering

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!

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.

  • 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 three species that are native to the region: Adelie, Chinstrap, and Gentoo, so 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.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Loading and examining the dataset
penguins_df = pd.read_csv("data/penguins.csv")
penguins_df
penguins_df = penguins_df.dropna()
penguins_df
penguins_df.isnull().sum()
penguins_df.describe()
import seaborn as sns
for i in penguins_df.columns[:-1]:
    ax=sns.boxplot(penguins_df[i])
    plt.show()
penguins_clean=penguins_df[(penguins_df['flipper_length_mm']>0) & (penguins_df['flipper_length_mm']<1000)]
penguins_clean.describe()
df = pd.get_dummies(penguins_clean).drop('sex_.',axis=1)
scaler = StandardScaler()
X=scaler.fit_transform(df)
penguins_preprocessed=pd.DataFrame(data=X,columns=df.columns)
penguins_preprocessed
‌
‌
‌