Beta
Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means clustering
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.head()
penguins_df.info()# Step 2 - Dealing with null values and outliers
penguins_df.boxplot()
plt.show()
penguins_df = penguins_df.dropna()
penguins_df[penguins_df['flipper_length_mm']>4000]
penguins_df[penguins_df['flipper_length_mm']<0]
penguins_clean = penguins_df.drop([9,14])# Step 3 - Perform preprocessing steps on the dataset to create dummy variables
df = pd.get_dummies(penguins_clean).drop('sex_.',axis=1)# Step 4 - Perform preprocessing steps on the dataset - scaling
scaler = StandardScaler()
X = scaler.fit_transform(df)
penguins_preprocessed = pd.DataFrame(data=X,columns=df.columns)
penguins_preprocessed.head(10)# Step 5 - Perform PCA
pca = PCA(n_components=None)
dfx_pca = pca.fit(penguins_preprocessed)
dfx_pca.explained_variance_ratio_
n_components=sum(dfx_pca.explained_variance_ratio_>0.1)
pca = PCA(n_components=n_components)
penguins_PCA = pca.fit_transform(penguins_preprocessed)# check to see the categorical feature in the dataset
penguins_clean.head()# Step 6 - Detect the optimal number of clusters for k-means clustering
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()
n_clusters=4# Step 7 - Run the k-means clustering algorithm
# with the optimal number of clusters
# and visualize the resulting clusters.
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()# Step 8 - Create a final statistical 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