Project: Clustering Antarctic Penguin Species

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! 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()

# 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])

print(penguins_clean)

# Step 3 - Perform preprocessing steps on the dataset to create dummy variables
df = pd.get_dummies(penguins_clean, columns=['sex'])
print(df)

# 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)

from sklearn.decomposition import PCA

# 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)

# 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', 'body_mass_g', 'label']
stat_penguins = penguins_clean[numeric_columns].groupby('label').mean()
stat_penguins