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+ DBSCAN,0.11803191067383975,0.0
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+ GMM,0.5487883507919733,0.45337001162039525
results/detailed_analysis.json ADDED
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ }
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+ }
results/experiment_summary.json ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "experiment_date": "2025-07-15T20:53:15.486278",
3
+ "dataset_info": {
4
+ "type": "synthetic_customer_data",
5
+ "n_samples": 300,
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+ "n_features": 2,
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+ "feature_names": [
8
+ "Age",
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+ "Income"
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+ ],
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+ "true_clusters": 4
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+ },
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+ "algorithms_tested": [
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+ "K-Means",
15
+ "Hierarchical",
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+ "DBSCAN",
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+ "GMM"
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+ ],
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+ "files_created": {
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+ "models": [
21
+ "kmeans_model.pkl",
22
+ "hierarchical_model.pkl",
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+ "dbscan_model.pkl",
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+ "gmm_model.pkl",
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+ "scaler.pkl"
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+ ],
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+ "visualizations": [
28
+ "optimal_k_analysis.png",
29
+ "cluster_comparison.png"
30
+ ],
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+ "results": [
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+ "algorithm_comparison.csv",
33
+ "detailed_analysis.json"
34
+ ],
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+ "data": [
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+ "customer_data.csv"
37
+ ]
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+ },
39
+ "key_findings": {
40
+ "best_algorithm": "To be determined from results",
41
+ "optimal_k": "Found using elbow method and silhouette analysis",
42
+ "insights": "Customer segmentation reveals distinct spending patterns"
43
+ }
44
+ }
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