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    Arctic Penguin Exploration: Unraveling Clusters in the Icy Domain with K-means clustering

    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!

    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.

    • 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 three species that are native to the region: Adelie, Chinstrap, and Gentoo, so 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.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Loading and examining the dataset
penguins_df = pd.read_csv("data/penguins.csv")
    penguins_df
    penguins_df = penguins_df.dropna()
    penguins_df
    penguins_df.isnull().sum()
    penguins_df.describe()
    import seaborn as sns
for i in penguins_df.columns[:-1]:
    ax=sns.boxplot(penguins_df[i])
    plt.show()
    penguins_clean=penguins_df[(penguins_df['flipper_length_mm']>0) & (penguins_df['flipper_length_mm']<1000)]
    penguins_clean.describe()
    df = pd.get_dummies(penguins_clean).drop('sex_.',axis=1)
scaler = StandardScaler()
X=scaler.fit_transform(df)
    penguins_preprocessed=pd.DataFrame(data=X,columns=df.columns)
    penguins_preprocessed
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