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Project: Clustering Antarctic Penguin Species
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!
0. Import the data and python libraries
# Import Required Packages
import pandas as pd
import numpy as np
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()1. Explatory Data Analysis
penguins_df.info()#crop types
penguins_df.sex.value_counts()peng_mean = penguins_df.groupby("sex").mean()
peng_mean.head(5)#for an example
colors = {'MALE':'tab:blue', 'FEMALE':'tab:orange'}
plt.scatter(x=penguins_df["culmen_length_mm"], y=penguins_df["culmen_depth_mm"],
c= penguins_df["sex"].map(colors));2. Data Preprocessing
penguins = pd.get_dummies(penguins_df, drop_first=True)
penguins.head()scaler = StandardScaler()
penguins_std = scaler.fit_transform(penguins)3. Clustering
inertia = []
n_clusters = np.arange(1,11)
for i in n_clusters:
model = KMeans(n_clusters=i)
model.fit(penguins_std)
inertia.append(model.inertia_)