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()

penguins_df.groupby('sex').agg('mean','max','min')

penguins_df.groupby('sex').count()

# check data to quantify the missing data and impute if necessary
penguins_df.info()

penguins_df = pd.get_dummies(penguins_df)
print(penguins_df.head())

# scale the data using Standard Scaler
scaler = StandardScaler()
scaled_df = scaler.fit_transform(penguins_df)

# KMeans Clustering
inertia = []
for cluster in range(1,8):
    model =  KMeans(n_clusters=cluster,random_state=42)
    model.fit(scaled_df)
    inertia.append(model.inertia_)
    
plt.plot(inertia)
plt.show()

# from the plot above, we see the inertia begins to decrease more slowly after 3
# therefore we will use n_clusters = 3

model = KMeans(n_clusters = 3, random_state=42)
model.fit(scaled_df)
labels = model.predict(scaled_df)

xs = scaled_df[:,0]
ys = scaled_df[:,2]
plt.scatter(xs,ys,c=labels)    
plt.show()

numeric_columns = list(penguins_df.columns[0:4])
print(numeric_columns)

penguins_df["label"] = labels
print(penguins_df.head())

stat_penguins= penguins_df.groupby('label')[numeric_columns].mean()
print(stat_penguins)