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Project: Clustering Antarctic Penguin Species
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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.shape
    penguins_df = penguins_df.dropna()
    import seaborn as sns
    
    # Show boxplot of each feature
    sns.boxplot(data=penguins_df)
    # Define upper threshold of feature flipper_length_mm
    upper_threshold = penguins_df['flipper_length_mm'].quantile(0.75) + 1.5 * (penguins_df['flipper_length_mm'].quantile(0.75) - penguins_df['flipper_length_mm'].quantile(0.25))
    lower_threshold = penguins_df['flipper_length_mm'].quantile(0.25) - 1.5 * (penguins_df['flipper_length_mm'].quantile(0.75) - penguins_df['flipper_length_mm'].quantile(0.25))
    penguins_clean = penguins_df[(penguins_df['flipper_length_mm'] >= lower_threshold) & (penguins_df['flipper_length_mm'] <= upper_threshold)]
    sns.boxplot(data = penguins_clean)
    # create Dummy for categorical variable and data separation
    pinguin_cat = pd.get_dummies(penguins_clean['sex'], prefix='sex').drop(columns = 'sex_.')
    penguins_numeric = penguins_clean.iloc[:,:4]
    new_penguin = pd.concat([penguins_numeric, pinguin_cat], axis=1)
    
    
    # Standarizing the Data
    penguins_preprocessed = StandardScaler().fit_transform(new_penguin)
    penguins_preprocessed = pd.DataFrame(penguins_preprocessed)
    # performing PCA
    
    model = PCA()
    model.fit(penguins_preprocessed)
    PCA_transformed = model.transform(penguins_preprocessed)
    model.components_.shape
    # Calculate the explained variance ratio
    explained_variance_ratio = model.explained_variance_ratio_
    
    # Find the number of components with more than 10% explained variance ratio
    num_components_above_threshold = (explained_variance_ratio > 0.1).sum()
    num_components_above_threshold