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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.

ColumnDescription
culmen_length_mmculmen length (mm)
culmen_depth_mmculmen depth (mm)
flipper_length_mmflipper length (mm)
body_mass_gbody mass (g)
sexpenguin 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.info()

#create dummy variables because machine learning works with numerical values
penguins_df = pd.get_dummies(penguins_df,columns = ["sex"],drop_first=True)
penguins_df.head()
from sklearn.preprocessing import StandardScaler

# the predicted values
scaler = StandardScaler()
scaled = scaler.fit_transform(penguins_df) # I am not predicting, I am clustering
#performing elblow analysis to determine the number of clusters to 
#elbow analysis is used on the original data not preprocessed data
inertia = []# inertia determine how close the clusters are in k_means 
ks = range(1,12)
for i in  ks: # random list of intertia
   kmeans= KMeans(n_clusters=i,random_state=42).fit(penguins_df)#perform k_means
   inertia.append(kmeans.inertia_)# get the inertia values
plt.plot(ks,inertia, '-o')#plot them
plt.xlabel("Number of Samples")
plt.ylabel("Inertia")
plt.title("A plot of to determine the optimal number of clusters")
plt.xticks(ks)
plt.show()



model = KMeans(n_clusters=3)  # from the above the optimal is 3
labels = model.fit_predict(scaled)
# print(label)
df = pd.DataFrame({
    "labels": labels,
    "culmen_depth_mm": penguins_df["culmen_depth_mm"]
})
plt.scatter(df["labels"], df["culmen_depth_mm"], c=model.labels_)
plt.title("A plot to show the different varietis of penguins")
plt.show()
penguins_df.columns
numeric_columns = ['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm',
       'body_mass_g'] # lables and their mean
penguins_df["label"] = model.labels_
stat_penguins = penguins_df.groupby("label")[numeric_columns].mean()
stat_penguins
penguins_df.head(20)