import numpy as np from sklearn.cluster import KMeans import matplotlib.pyplot as plt # Sample data: customer_id, total_purchases, avg_time_between_purchases (hours) customer_data = np.array([ [1, 25, 0.5], [2, 10, 5], [3, 15, 2], [4, 30, 1], [5, 7, 10], [6, 20, 3], [7, 12, 7], [8, 28, 0.8], [9, 6, 12], [10, 18, 1.5] ]) # Extract customer IDs and feature data customer_ids = customer_data[:, 0].astype(int) features = customer_data[:, 1:] # Perform k-means clustering n_clusters = 2 kmeans = KMeans(n_clusters=n_clusters, random_state=42) cluster_labels = kmeans.fit_predict(features) # Find the cluster with the lowest average time between purchases impulsive_cluster = np.argmin(kmeans.cluster_centers_[:, 1]) # Get the customer IDs in the impulsive cluster impulsive_buyers = customer_ids[cluster_labels == impulsive_cluster] print("Impulsive buyers:", impulsive_buyers) # Visualize the clustering results (optional) plt.scatter(features[:, 0], features[:, 1], c=cluster_labels, cmap='viridis') plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], c='red', marker='x') plt.xlabel('Total Purchases') plt.ylabel('Average Time Between Purchases (hours)') plt.show()
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AVR DT

AVR DT

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  • Capture the essence of downtown Atlanta with this iconic photo from Avos Records. Featuring breathtaking city views, this image is perfect for decorating any home or office and adds a sense of vibrancy to your space!

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