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['sex'] = penguins_df['sex'].map({'MALE': 1, 'FEMALE': 0})
print(penguins_df.info())
# Instantiate StandardScaler
scaler = StandardScaler()
# Tranforming penguins_df
penguins_scaled = scaler.fit_transform(penguins_df)
# Create lists
cluster_list = list(range(1, 10))
inertia_list = []
# filling inertia list
for i in cluster_list:
kmeans = KMeans(n_clusters=i)
inertia = kmeans.fit(penguins_scaled).inertia_
inertia_list.append(inertia)
# PLotting Inertia vs Number of clusters
plt.plot(cluster_list, inertia_list, marker='v')
plt.xlabel('Number of Clusters')
plt.ylabel('Inertia')
plt.show()From the line plot above, the optimal number of clusters is 5
# Instantiating KMeans with the optimal cluster number of 5
kmeans = KMeans(n_clusters=5)
features = kmeans.fit_predict(penguins_df)
stat_penguins = pd.DataFrame(features)
print(stat_penguins)