""" Customer Segmentation — K-Means RFM + DBSCAN Anomaly Detection ================================================================ CLO5: K-Means, K-Medoids, DBSCAN 1. RFM Analysis (Recency, Frequency, Monetary) 2. K-Means Clustering with Elbow + Silhouette 3. DBSCAN for anomaly/outlier detection 4. Segment profiling & business recommendations Usage: python analytics/customer_segmentation.py --data-dir ./data/raw """ import os, sys, logging, argparse import pandas as pd import numpy as np from sklearn.cluster import KMeans, DBSCAN from sklearn.preprocessing import StandardScaler from sklearn.metrics import silhouette_score import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import warnings warnings.filterwarnings('ignore') logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s') logger = logging.getLogger(__name__) def compute_rfm(data_dir: str) -> pd.DataFrame: """Tính RFM từ Olist dataset.""" orders = pd.read_csv(os.path.join(data_dir, 'olist_orders_dataset.csv'), parse_dates=['order_purchase_timestamp']) items = pd.read_csv(os.path.join(data_dir, 'olist_order_items_dataset.csv')) customers = pd.read_csv(os.path.join(data_dir, 'olist_customers_dataset.csv')) # Filter delivered orders orders = orders[orders['order_status'] == 'delivered'].dropna(subset=['order_purchase_timestamp']) # Merge order_revenue = items.groupby('order_id').agg(revenue=('price', 'sum')).reset_index() merged = orders.merge(order_revenue, on='order_id', how='left') merged = merged.merge(customers[['customer_id', 'customer_unique_id', 'customer_state']], on='customer_id', how='left') # Reference date ref_date = merged['order_purchase_timestamp'].max() + pd.Timedelta(days=1) # RFM rfm = merged.groupby('customer_unique_id').agg( recency=('order_purchase_timestamp', lambda x: (ref_date - x.max()).days), frequency=('order_id', 'nunique'), monetary=('revenue', 'sum'), state=('customer_state', 'first'), ).reset_index() rfm['monetary'] = rfm['monetary'].round(2) logger.info(f"[RFM] {len(rfm)} unique customers") logger.info(f" Recency: mean={rfm['recency'].mean():.0f}, median={rfm['recency'].median():.0f}") logger.info(f" Frequency: mean={rfm['frequency'].mean():.2f}, max={rfm['frequency'].max()}") logger.info(f" Monetary: mean={rfm['monetary'].mean():.0f}, median={rfm['monetary'].median():.0f}") return rfm def kmeans_segmentation(rfm: pd.DataFrame, output_dir: str): """K-Means clustering with optimal K selection.""" features = ['recency', 'frequency', 'monetary'] X = rfm[features].copy() # Handle outliers — cap at 99th percentile for col in features: p99 = X[col].quantile(0.99) X[col] = X[col].clip(upper=p99) scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # Elbow + Silhouette K_range = range(2, 9) inertias, silhouettes = [], [] for k in K_range: km = KMeans(n_clusters=k, random_state=42, n_init=10, max_iter=300) labels = km.fit_predict(X_scaled) inertias.append(km.inertia_) sil = silhouette_score(X_scaled, labels) silhouettes.append(sil) logger.info(f" K={k}: Inertia={km.inertia_:.0f}, Silhouette={sil:.4f}") best_k = list(K_range)[np.argmax(silhouettes)] logger.info(f" Best K by Silhouette: {best_k}") # Final model km_final = KMeans(n_clusters=best_k, random_state=42, n_init=10) rfm['cluster'] = km_final.fit_predict(X_scaled) # RFM scoring (quintiles) rfm['r_score'] = pd.qcut(rfm['recency'], q=5, labels=[5, 4, 3, 2, 1], duplicates='drop').astype(int) rfm['f_score'] = pd.qcut(rfm['frequency'].rank(method='first'), q=5, labels=[1, 2, 3, 4, 5], duplicates='drop').astype(int) rfm['m_score'] = pd.qcut(rfm['monetary'].rank(method='first'), q=5, labels=[1, 2, 3, 4, 5], duplicates='drop').astype(int) # Name clusters profiles = rfm.groupby('cluster').agg( count=('customer_unique_id', 'count'), avg_recency=('recency', 'mean'), avg_frequency=('frequency', 'mean'), avg_monetary=('monetary', 'mean'), ).round(1) segment_names = {} for idx, row in profiles.iterrows(): if row['avg_recency'] < profiles['avg_recency'].median() and row['avg_frequency'] > profiles['avg_frequency'].median(): if row['avg_monetary'] > profiles['avg_monetary'].median(): segment_names[idx] = 'Champions' else: segment_names[idx] = 'Loyal' elif row['avg_recency'] < profiles['avg_recency'].median(): segment_names[idx] = 'New/Promising' elif row['avg_frequency'] > profiles['avg_frequency'].median(): segment_names[idx] = 'At Risk' else: segment_names[idx] = 'Lost/Hibernating' rfm['segment'] = rfm['cluster'].map(segment_names) profiles['segment'] = profiles.index.map(segment_names) print(f"\n CUSTOMER SEGMENTS (K={best_k}):") print(profiles.to_string()) # Visualization fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # Elbow axes[0, 0].plot(list(K_range), inertias, 'bo-') axes[0, 0].set_xlabel('K') axes[0, 0].set_ylabel('Inertia') axes[0, 0].set_title('Elbow Method') # Silhouette axes[0, 1].plot(list(K_range), silhouettes, 'ro-') axes[0, 1].set_xlabel('K') axes[0, 1].set_ylabel('Silhouette Score') axes[0, 1].set_title('Silhouette Method') axes[0, 1].axvline(x=best_k, color='green', linestyle='--', label=f'Best K={best_k}') axes[0, 1].legend() # Scatter: Recency vs Monetary colors = ['#e74c3c', '#3498db', '#2ecc71', '#f39c12', '#9b59b6', '#1abc9c', '#e67e22'] for cluster in range(best_k): mask = rfm['cluster'] == cluster name = segment_names.get(cluster, f'C{cluster}') axes[1, 0].scatter(rfm.loc[mask, 'recency'], rfm.loc[mask, 'monetary'], c=colors[cluster % len(colors)], label=name, alpha=0.4, s=15) axes[1, 0].set_xlabel('Recency (days)') axes[1, 0].set_ylabel('Monetary (BRL)') axes[1, 0].set_title('Customer Segments') axes[1, 0].legend(fontsize=8) # Segment size bar chart seg_counts = rfm['segment'].value_counts() seg_counts.plot(kind='bar', ax=axes[1, 1], color=[colors[i % len(colors)] for i in range(len(seg_counts))], alpha=0.8) axes[1, 1].set_title('Segment Size') axes[1, 1].set_ylabel('Customers') axes[1, 1].tick_params(axis='x', rotation=30) plt.suptitle('K-Means Customer Segmentation (RFM)', fontsize=14, fontweight='bold') plt.tight_layout() path = os.path.join(output_dir, 'customer_segmentation.png') plt.savefig(path, dpi=150, bbox_inches='tight') plt.close() logger.info(f"[VIZ] Saved: {path}") return rfm def dbscan_anomaly(rfm: pd.DataFrame, output_dir: str): """DBSCAN để phát hiện anomaly customers.""" features = ['recency', 'frequency', 'monetary'] X = rfm[features].copy() for c in features: X[c] = X[c].clip(upper=X[c].quantile(0.99)) scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # DBSCAN db = DBSCAN(eps=0.8, min_samples=10) labels = db.fit_predict(X_scaled) n_clusters = len(set(labels)) - (1 if -1 in labels else 0) n_noise = (labels == -1).sum() rfm['dbscan_label'] = labels rfm['is_anomaly'] = (labels == -1).astype(int) logger.info(f"[DBSCAN] Clusters: {n_clusters}, Noise/Anomalies: {n_noise} ({n_noise/len(rfm)*100:.1f}%)") # Profile anomalies vs normal anomalies = rfm[rfm['is_anomaly'] == 1] normal = rfm[rfm['is_anomaly'] == 0] print(f"\n DBSCAN ANOMALY DETECTION:") print(f" Normal customers: {len(normal):,}") print(f" Anomaly customers: {len(anomalies):,} ({len(anomalies)/len(rfm)*100:.1f}%)") print(f"\n Anomaly profile vs Normal:") for feat in features: print(f" {feat}: Anomaly avg={anomalies[feat].mean():.1f} vs Normal avg={normal[feat].mean():.1f}") # Visualization fig, ax = plt.subplots(figsize=(10, 6)) ax.scatter(normal['recency'], normal['monetary'], c='#3498db', alpha=0.3, s=10, label='Normal') ax.scatter(anomalies['recency'], anomalies['monetary'], c='#e74c3c', alpha=0.8, s=40, marker='x', label=f'Anomaly ({len(anomalies)})') ax.set_xlabel('Recency (days)') ax.set_ylabel('Monetary (BRL)') ax.set_title('DBSCAN Anomaly Detection on Customer RFM') ax.legend() path = os.path.join(output_dir, 'dbscan_anomaly_customers.png') plt.savefig(path, dpi=150, bbox_inches='tight') plt.close() logger.info(f"[VIZ] Saved: {path}") return rfm def main(): parser = argparse.ArgumentParser(description='Customer Segmentation') parser.add_argument('--data-dir', type=str, default='./data/raw') parser.add_argument('--output-dir', type=str, default='./data/analytics') args = parser.parse_args() os.makedirs(args.output_dir, exist_ok=True) rfm = compute_rfm(args.data_dir) rfm = kmeans_segmentation(rfm, args.output_dir) rfm = dbscan_anomaly(rfm, args.output_dir) rfm.to_csv(os.path.join(args.output_dir, 'customer_segments.csv'), index=False) # Business recommendations print(f"\n{'='*70}") print(f" BUSINESS RECOMMENDATIONS") print(f"{'='*70}") for seg in rfm['segment'].unique(): n = (rfm['segment'] == seg).sum() avg_m = rfm.loc[rfm['segment'] == seg, 'monetary'].mean() recs = { 'Champions': 'Reward program, early access to new products', 'Loyal': 'Upsell premium products, referral program', 'New/Promising': 'Onboarding emails, first purchase discount', 'At Risk': 'Win-back campaign, special offers', 'Lost/Hibernating': 'Re-engagement email, survey for feedback', } print(f" {seg}: {n:,} customers, avg spend BRL {avg_m:,.0f}") print(f" → {recs.get(seg, 'Standard marketing')}") print(f"{'='*70}") if __name__ == '__main__': main()