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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")

Inspecting data

penguins_df.describe()
penguins_df.info()
penguins_df
penguins_df.isna().sum()
penguins_df[ penguins_df.isna().any(axis=1) ]
  • We have two entries with only null values as features.
  • We have seven entries with the value null as sex
# Notice that the categorical row have a non valid value
penguins_df["sex"].unique()

Cleaning data by removing null rows

penguins_df = penguins_df.dropna().reset_index(drop=True)
# Verifying that null rows are removed
penguins_df.isna().sum()
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