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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!
# 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
df = pd.read_csv("penguins.csv")
df.head()#adding dummy variables
df=pd.get_dummies(df,columns=["sex"],drop_first=True)
df.head()#Standardizing the dataset
scaler=StandardScaler()
sc=scaler.fit_transform(df)
df_scaled=pd.DataFrame(sc,columns=df.columns)
df_scaled.head()#Performing Elbow analysis
inertia=[]
for i in range(1,11,1):
km=KMeans(n_clusters=i,random_state=42).fit(df_scaled)
inertia.append(km.inertia_)# Plotting inertia values to determine the optimal number of clusters for the dataset
plt.plot(inertia)
plt.title("inertia values for each number of cluster")# training the model using best inertia value which is 5
Kn=KMeans(n_clusters=5,random_state=42).fit(df_scaled)
labels=Kn.labels_
labels
#creatning a scatterplot of the labels vs culmen_length_mm
plt.scatter(labels,penguins_df['culmen_length_mm'])
plt.title("labels vs culmen_length_mm")
plt.xlabel("labels")# Creating a list of numeric (non-binary) columns
numeric_columns = penguins_df.select_dtypes(include=["number"]).nunique()
numeric_columns = numeric_columns[numeric_columns > 2].index.tolist()
# Adding cluster labels to the DataFrame
penguins_df["label"] = Kn.labels_
# Aggregating numeric data by cluster (label)
stat_penguins = penguins_df.groupby("label")[numeric_columns].mean()
stat_penguins