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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.

ColumnDescription
culmen_length_mmculmen length (mm)
culmen_depth_mmculmen depth (mm)
flipper_length_mmflipper length (mm)
body_mass_gbody mass (g)
sexpenguin 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()
penguins_df = pd.get_dummies(penguins_df, drop_first=True, dtype=int)
samples = penguins_df.values
scalar = StandardScaler()
st_samples = scalar.fit_transform(samples)
ks = range(1, 8)
inertias = []

for k in ks:
    # Create a KMeans instance with k clusters: model
    model = KMeans(n_clusters=k, random_state=42)
    
    # Fit model to samples
    model.fit(st_samples)
    
    # 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.xticks(ks)
plt.show()
# Create a KMeans instance with 3 clusters: model
model = KMeans(n_clusters = 4)

# Fit model to points
model.fit(st_samples)

# Determine the cluster labels of new_points: labels
labels = model.predict(st_samples)
# Assign the columns of new_points: xs and ys
xs = st_samples[:,0]
ys = st_samples[:,1]

# Make a scatter plot of xs and ys, using labels to define the colors
plt.scatter(xs, ys, c=labels, alpha=0.5)

# Assign the cluster centers: centroids
centroids = model.cluster_centers_

# Assign the columns of centroids: centroids_x, centroids_y
centroids_x = centroids[:,0]
centroids_y = centroids[:,1]

# Make a scatter plot of centroids_x and centroids_y
plt.scatter(centroids_x, centroids_y,marker="D", s=50)
plt.show()


numeric_columns = ['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g']
label = model.labels_
penguins_df["label"] = label
stat_penguins = penguins_df.groupby('label')[numeric_columns].mean()