Skip to content
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()#Finding Nulls
print(penguins_df.head())
print(penguins_df.info())
print(penguins_df.isnull().sum())
#dropping NaNs
penguins_df = penguins_df.dropna()#Finding Outliers
penguins_df.boxplot()
plt.show()#Removing Outliers
print(penguins_df[penguins_df['flipper_length_mm']>4000],
penguins_df[penguins_df['flipper_length_mm']<0])
penguins_clean = penguins_df.drop([9,14])
penguins_clean.boxplot()
plt.show()# Create dummy variables
df = pd.get_dummies(penguins_clean, columns=['sex'])
df.head()#Pre-Processing data for PCA
scaler = StandardScaler()
X = scaler.fit_transform(df)
penguins_preprocessed = pd.DataFrame(data=X,columns=df.columns)
penguins_preprocessed.head(10)# Performing PCA
%matplotlib inline
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
pca = PCA()
pca.fit(penguins_preprocessed)
exp_variance = pca.explained_variance_ratio_
# Plotting explained variance
fig, ax = plt.subplots()
ax.bar(range(pca.n_components_), exp_variance)
ax.set_xlabel('Principal Component #')
ax.axhline(y=0.1, linestyle='--')#Graph shows only features 0 and 1 are above 10%
#Running PCA with only these features
n_components = 2
pca = PCA(n_components=n_components, random_state=42)
penguins_PCA = pca.fit_transform(penguins_preprocessed)#Finding optimum number of clusters
inertia = []
for k in range(1,10):
kmeans= KMeans(n_clusters=k, random_state=42).fit(penguins_PCA)
inertia.append(kmeans.inertia_)
plt.plot(range(1, 10), inertia, marker='o')
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
plt.title('Elbow Method')
plt.show()# Define the number of clusters
n_clusters = 3 # You can change this number based on your requirement
# Running KMeans cluster algorithm
kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(penguins_PCA)
plt.scatter(penguins_PCA[:, 0], penguins_PCA[:, 1], c=kmeans.labels_, cmap='viridis')
plt.xlabel('First Principal Component')
plt.ylabel('Second Principal Component')
plt.title(f'K-means Clustering (K={n_clusters})')
plt.legend()
plt.show()#Creating a dataframe for each cluster
penguins_clean['label']= kmeans.labels_
numeric_columns = ['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm',
'label']
stat_penguins = penguins_clean[numeric_columns].groupby('label').mean()
stat_penguins