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
seed = 10
# Loading and examining the dataset
penguins_df = pd.read_csv("penguins.csv")
penguins_df.head()# preprocessing
df = pd.get_dummies(penguins_df)
df.head()# scaling numeric columns
numeric_cols = ['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g']
scaler = StandardScaler()
df[numeric_cols] = scaler.fit_transform(df[numeric_cols])
df.head()# determine optimal number of clusters
inertia = []
for k in range(1,10):
kmeans = KMeans(n_clusters=k, random_state=seed)
kmeans.fit(df)
inertia.append(kmeans.inertia_)
plt.plot(range(1,10), inertia, marker='x')
plt.title('Elbow Analysis')
plt.ylabel('Inertia')
plt.xlabel('Number of Clusters')
plt.show()After the 3rd cluster, the inertia started to decrease more slowly so three(3) is the optimum number of clusters
# fitting the model
kmeans = KMeans(n_clusters=3, random_state=seed)
kmeans.fit(df)
df['label'] = kmeans.labels_
# visualise clusters
plt.figure(figsize=(15, 10))
for index, col in enumerate(df.columns[:-3]):
plt.subplot(2, 2, index + 1)
plt.scatter(df['label'], df[col], c=kmeans.labels_)
plt.title(f'clusters in {col}')
plt.show()# creating the submission dataframe
stat_penguins = penguins_df[numeric_cols]
stat_penguins['label'] = kmeans.labels_
stat_penguins = stat_penguins.groupby(['label'])[numeric_cols].mean()
stat_penguins