Clustering

gistfile1.txt

from sklearn.cluster import KMeans

# Clustering
# Number of clusters
n_clusters = 4
kmeans = KMeans(n_clusters=n_clusters, random_state=0)
clusters = kmeans.fit_predict(combined_top_items_daily)

# Creating a DataFrame for the PCA components
pca_df = pd.DataFrame(principal_components_combined, columns=[f'PC{i+1}' for i in range(n_components_combined)])
pca_df['Cluster'] = clusters

# Explained variance
explained_variance = pca_combined.explained_variance_ratio_
cumulative_explained_variance = np.cumsum(explained_variance)

def biplot_with_shapes_and_legend(score, coeff, clusters, labels=None):
    xs = score[:,0]
    ys = score[:,1]
    n = coeff.shape[0]  # Number of components
    scalex = 1.0/(xs.max() - xs.min())
    scaley = 1.0/(ys.max() - ys.min())
    
    # Scatter plot with different shapes for each cluster
    shapes = ['o', 's', '^', 'D']  # Different shapes for each cluster
    for cluster in range(n_clusters):
        plt.scatter(xs[clusters == cluster] * scalex, ys[clusters == cluster] * scaley, 
                    c=np.random.rand(3,), marker=shapes[cluster], label=f'Cluster {cluster + 1}')

    # Plotting arrows and labels for each principal component
    for i in range(n):
        plt.arrow(0, 0, coeff[i,0], coeff[i,1], color='r', alpha=0.5)
        if labels is not None and i < len(labels):
            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, labels[i], color='g', ha='center', va='center')

    # Adding a legend
    plt.legend()

# Plotting the modified biplot
plt.figure(figsize=(12, 8))
biplot_with_shapes_and_legend(principal_components_combined, np.transpose(pca_combined.components_[0:2, :]), clusters, labels=daily_top_items_sales_with_total.columns)
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.title('Biplot of PCA (Including Total Daily Sales) with Clusters')
plt.show()