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()
top of page
Unisex midweight hoodie
Looking for the perfect high-quality hoodie? This unisex hoodie is a mix of comfort and function. Soft and cozy on the inside, sleek and stylish on the outside.

• Unisex fit
• 80% cotton, 20% polyester blend fleece
• 100% cotton face
• Fabric weight: 8.5 oz./yd² (280 g/m²)
• Jersey-lined hood
• Split stitch double-needle sewing on all seams
• Twill neck tape
• 1 × 1 ribbing for cuffs and waistband
• Metal eyelets
• Blank product sourced from Pakistan

This product is made especially for you as soon as you place an order, which is why it takes us a bit longer to deliver it to you. Making products on demand instead of in bulk helps reduce overproduction, so thank you for making thoughtful purchasing decisions!

Unisex midweight hoodie

$36.50Price
Excluding Sales Tax
Quantity
    Product Page: Stores_Product_Widget
    bottom of page