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import joblib
import pandas as pd
kmeans = joblib.load('artifacts/kmeans.pkl')
scaler = joblib.load('artifacts/scaler.pkl')
CLUSTER_INFO = {
0: {
"name": "High-Value Loyal Shoppers",
"description": "High income, high total spending, prefers in-store shopping, moderately recent purchases.",
"recommendation": "Offer exclusive in-store experiences, VIP loyalty tiers, early access to new collections, and personalized concierge service."
},
1: {
"name": "Budget-Conscious Occasional Shoppers",
"description": "Low income, low spending, high web browsing, but made a very recent purchase.",
"recommendation": "Target with limited-time discounts, entry-level product bundles, and personalized email offers based on browsing history to encourage repeat purchases."
},
2: {
"name": "Mid-Tier Engaged Browsers",
"description": "Mid-range income, low spending despite frequent website visits; hasn’t purchased in a long time.",
"recommendation": "Re-engage with cart abandonment reminders, free shipping thresholds, or 'we miss you' incentives (e.g., 15% off). Highlight bestsellers and social proof to drive conversion."
},
3: {
"name": "Active Online-Focused Shoppers",
"description": "High income, high spending, shops frequently both online and in-store—with strongest activity on web.",
"recommendation": "Offer premium bundles, omnichannel loyalty rewards (e.g., buy online, pick up in-store + bonus points), and personalized cross-channel recommendations."
}
}
def predict(age, income, total_spending, num_web_purchases, num_store_purchases, num_web_visits, recency):
input_data = pd.DataFrame({
"Age": [age],
"Income": [income],
"Total_Spendings": [total_spending],
"NumWebPurchases": [num_web_purchases],
"NumStorePurchases": [num_store_purchases],
"NumWebVisitsMonth": [num_web_visits],
"Recency": [recency]})
scaled_data = scaler.transform(input_data)
cluster_id = kmeans.predict(scaled_data)[0]
info = CLUSTER_INFO[cluster_id]
return {
"cluster_id": int(cluster_id),
"cluster_name": info["name"],
"description": info["description"],
"recommendation": info["recommendation"]
}
"""if __name__ == "__main__":
result = predict(45, 60000, 900, 6, 8, 5, 40)
print(f"✨ Customer Segment: {result['cluster_name']} (ID: {result['cluster_id']})")
print(f"📝 Profile: {result['description']}")
print(f"🎯 Marketing Action: {result['recommendation']}")"""
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