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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")
print(penguins_df.head())
print(penguins_df.info())#converting categorical variables into dummy variables
penguins_df = pd.get_dummies(penguins_df, dtype = "int")
print(penguins_df)#scaling variables
scaler = StandardScaler()
X = scaler.fit_transform(penguins_df)
penguins_preprocessed = pd.DataFrame(data = X, columns = penguins_df.columns)
print(penguins_preprocessed.head(10))#detecting the optimal no. of clusters for k-means clustering
inertia = []
for k in range(1,10):
kmeans = KMeans(n_clusters=k, random_state=42).fit(penguins_preprocessed)
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#running the k-means clustering algorithm with the optimal no. of clusters
kmeans = KMeans(n_clusters = n_clusters, random_state = 42).fit(penguins_preprocessed)
penguins_df["label"] = kmeans.labels_# visualizing the clusters
plt.scatter(penguins_df["label"], penguins_df["culmen_length_mm"], c=kmeans.labels_, cmap="viridis")
plt.xlabel("Cluster")
plt.ylabel("culmen_length_mm")
plt.title(f"K-means Clustering (K={n_clusters})")
plt.show()#creating final "stat_penguins" Dataframe
numeric_columns = ["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm", "label"]
stat_penguins = penguins_df[numeric_columns].groupby("label").mean()
print(stat_penguins)