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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")
    # Explore the data
    print(penguins_df.head())
    print(penguins_df.info())
    
    # Drop null values
    penguins_df = penguins_df.dropna()
    
    # Identify outliers using boxplot
    penguins_df.boxplot()
    plt.show()
    # Remove outliers (values outside the whiskers)
    penguins_df[penguins_df['flipper_length_mm']>4000]
    penguins_df[penguins_df['flipper_length_mm']<0]
    penguins_clean = penguins_df.drop([9,14])
    
    # Create dummy variables
    df = pd.get_dummies(penguins_clean).drop('sex_.',axis=1)
    
    # Standardize the data
    scaler = StandardScaler()
    penguins_preprocessed = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
    
    # Perform PCA without specifying the number of components
    pca = PCA()
    penguins_PCA = pca.fit(penguins_preprocessed)
    
    # Determine the number of components with more than 10% explained variance ratio
    n_components = sum(penguins_PCA.explained_variance_ratio_ > 0.1)
    # Run PCA again with the optimal number of components
    pca = PCA(n_components=n_components)
    penguins_PCA = pca.fit_transform(penguins_preprocessed)
    
    # Perform Elbow analysis
    inertia = []
    for k in range(1, 10):
        kmeans = KMeans(n_clusters=k, random_state=42).fit(penguins_PCA)
        inertia.append(kmeans.inertia_)
    
    # Visualize the inertia values
    plt.plot(range(1, 10), inertia)
    plt.xlabel('Number of clusters')
    plt.ylabel('Inertia')
    plt.show()
    # Determine the optimal number of clusters (where the inertia begins to decrease more slowly)
    n_clusters = 4
    
    # Run k-means clustering algorithm with the optimal number of clusters
    kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(penguins_PCA)
    
    # Visualize the defined clusters
    plt.scatter(penguins_PCA[:, 0], penguins_PCA[:, 1], c=kmeans.labels_)
    plt.show()
    
    # Add a new column named 'label' to the penguins_clean dataset
    penguins_clean['label'] = kmeans.labels_
    
    # Create a list containing the names of the numeric columns
    numeric_columns = ['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm','label']
    
    # Create a final characteristic DataFrame
    stat_penguins = penguins_clean.groupby('label')[numeric_columns].mean()
    stat_penguins