Project: Clustering Antarctic Penguin Species

Clustering Antarctic Penguin Species

Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means Clustering

Alt text source: @allison_horst https://github.com/allisonhorst/penguins

Project Description

Delve into the information about penguins by utilizing unsupervised learning techniques on a thoughtfully curated dataset. uncover concealed patterns, clusters, and relationships that exist within the dataset.

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!

Utilize unsupervised learning skills to clusters in the penguins dataset!

Import, investigate and pre-process the "penguins.csv" dataset.
Perform a cluster analysis based on a reasonable number of clusters and collect the average values for the clusters. The output should be a DataFrame named stat_penguins with one row per cluster that shows the mean of the original variables (or columns in "penguins.csv") by cluster. stat_penguins should not include any non-numeric columns.

# 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()

display(penguins_df.info())
display(penguins_df.isna().sum())

display(penguins_df['sex'].value_counts())

1. Perform preprocessing steps on the dataset to create dummy variables

Create dummy variables for the available categorical feature in the dataset, then drop the original column.

# Perform preprocessing steps on the dataset to create dummy variables
# Using get dummies method on the categorical feature
# Convert categorical variables into dummy/indicator variables

pre_penguin = pd.get_dummies(penguins_df, drop_first=True)
pre_penguin.head()

# Perform preprocessing steps on the dataset - standarizing/scaling
# Standardizing (also called scaling) a dataset before clustering can improve accuracy and is generally recommended
# Scaling variables (also called standardizing) is recommended before performing a clustering algorithm since this can increase the performance greatly (see https://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html)

scaler = StandardScaler()
X = scaler.fit_transform(pre_penguin)
penguins_preprocessed = pd.DataFrame(data=X,columns=pre_penguin.columns)
penguins_preprocessed.head(10)

2. Detect the optimal number of clusters for k-means clustering

Perform Elbow analysis to determine the optimal number of clusters for this dataset.

ks = range(1,10)
inertias=[]

for k in ks:
    # Create a KMeans instance with k clusters: model
    # and Fit model to penguins_preprocessed
    model = KMeans(n_clusters=k, random_state=42).fit(penguins_preprocessed)
    
    # Append the inertia to the list of inertias
    inertias.append(model.inertia_)
    
# Plot ks vs inertias
plt.plot(ks, inertias, '-o')
plt.xlabel('number of clusters, k')
plt.ylabel('inertia')
plt.title('Elbow Method')
plt.xticks(ks)
plt.show()

3. Run the k-means clustering algorithm

Using the optimal number of clusters obtained from the previous step, run the k-means clustering algorithm once more on the preprocessed data.

n_clusters=4

# Run the k-means clustering algorithm
# with the optimal number of clusters 

kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(penguins_preprocessed)
penguins_df['label'] = kmeans.labels_
penguins_df['label'].value_counts()