Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means clustering
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!
source: @allison_horst https://github.com/allisonhorst/penguins
# 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.head()penguins_df.info()penguins_df.describe()penguins_df.isna().sum()penguins_df.dropna(inplace= True)penguins_df.boxplot()
plt.show()penguins_df[penguins_df['flipper_length_mm'] > 4000]penguins_df[penguins_df['flipper_length_mm'] < 0]penguins_clean = penguins_df.drop([9,14])Pre-process the cleaned data using standard scaling and the one-hot encoding to add dummy variables:
df = pd.get_dummies(penguins_clean).drop('sex_.', axis = 1)