Clustering Modeling Pipeline Guidebook

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🎯 Purpose

This guidebook provides a modular, analyst-ready pipeline for building, evaluating, and interpreting unsupervised clustering models in Python. It supports KMeans, DBSCAN, GMM, and hierarchical clustering workflows with structure and clarity.

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🔁 1. Pipeline Overview

[ Phase 1: Imports + Config ]
[ Phase 2: Load + Prep ]
[ Phase 3: EDA + Scaling ]
[ Phase 4: Model + Tune ]
[ Phase 5: Evaluate + Visualize ]
[ Phase 6: Export + Label ]

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⚙️ 2. Clustering Skeleton (Generalized Workflow)

# Phase 1: Imports
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns

# Phase 2: Load
df = pd.read_csv("cleaned_features.csv")

# Phase 3: Scale (Required!)
X = df.select_dtypes(include='number')
X_scaled = StandardScaler().fit_transform(X)

# Phase 4: Model
kmeans = KMeans(n_clusters=4, random_state=42)
kmeans.fit(X_scaled)
df['cluster'] = kmeans.labels_

# Phase 5: Evaluate
sns.pairplot(df, hue='cluster')

# Phase 6: Export
df.to_csv("clustered_output.csv", index=False)

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🧪 3. Model-Specific Adjustments

Model	Key Params	Notes
KMeans	n_clusters	Use elbow/gap method to select k
DBSCAN	eps, min_samples	Use k-distance plot to tune
GMM	n_components, cov_type	Use AIC/BIC for model selection
Agglomerative	n_clusters, linkage	Pair with dendrogram plot

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📈 4. Evaluation Visuals

• Pairplots / scatter plots (sns.pairplot, sns.scatterplot)
• Cluster heatmap of averages (mean value per cluster)
• Silhouette plots (for cohesion vs separation)
• PCA/UMAP dimensionality reduction (for visualization)

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🧠 5. Labeling & Profiling Clusters

# Group by cluster for summary stats
cluster_profiles = df.groupby('cluster').mean()

• Add meaningful labels if possible (e.g., “High spenders”, “Inactive users”)
• Save cluster mapping or apply to new data with saved scaler + model

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✅ Clustering Pipeline Checklist

• [ ] Numeric features scaled
• [ ] Model parameters selected with visual/tuning logic
• [ ] Evaluation includes plots and silhouette scores
• [ ] Clusters labeled or described meaningfully
• [ ] Final output saved to /outputs/ with cluster column

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💡 Tip

“Clustering is pattern discovery — label what matters, and always show the shape.”