Cluster analysis - Wholesale customers data set
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This document analyzes customer spending data from wholesale customers to identify spending clusters. It finds strong correlations between certain spending categories. Hierarchical and k-means clustering both identify around 3 main clusters. Cluster 1 consists of high spenders on milk, grocery and detergent products who tend to use the retail channel. Cluster 2 has high fresh and frozen spenders who are more likely to use the hotel/restaurant channel. Cluster 3 contains low overall spenders. Analyzing spending on grocery, detergent and milk captures most of the variability in the data.
Cluster analysis - Wholesale customers data set
- 1. Wholesale Customer Data Clustering By - Divya Ganjoo
- 2. Data https://archive.ics.uci.edu/ml/datasets/Wholesale+customers# Attribute Information: 1) FRESH: annual spending (m.u.) on fresh products (Continuous); 2) MILK: annual spending (m.u.) on milk products (Continuous); 3) GROCERY: annual spending (m.u.)on grocery products (Continuous); 4) FROZEN: annual spending (m.u.)on frozen products (Continuous) 5) DETERGENTS_PAPER: annual spending (m.u.) on detergents and paper products (Continuous) 6) DELICATESSEN: annual spending (m.u.)on and delicatessen products (Continuous); 7) CHANNEL: customers’ Channel - Horeca (Hotel/Restaurant/Café) or Retail channel (Nominal) 8) REGION: customers’ Region – Lisnon, Oporto or Other (Nominal)
- 3. Exploring data ● Exploring the data reveals that channels contain more variability than the regions (Example graph above). Similar patterns emerge for other variables. ● Strong correlation is found in - Grocery & Detergent - 0.92, Milk & Det - 0.66 and Milk & Grocery - 0.73 ● Looking at various graphs such as the one above, we can roughly estimate 3 clusters
- 4. Hierarchical ● Different hierarchical clusterings dendrograms roughly categorize data into 3 or 4 groups ● Some observations seem to be separated out consistently (even though its hard to read here) ● Above dendrogram based on Milk and Grocery AND Milk and Det_Paper Fig: Dendrogram: Milk and Grocery Fig: Dendrogram: Milk and Detergent/Paper Fig: Heatmap: Milk and Grocery
- 5. K-means ● 3 seems to be the optimum value for no. of clusters as seen from the plot of Within SS against no. of clusters ● Running kmeans with cluster size = 3, we get the following centers >>>
- 6. Conclusion Cluster1: High Milk, Grocery, Det_ Paper Cluster2: High Fresh, Frozen, Deli Cluster 3: Low Spenders Channel 1 Channel 2 Cluster 1 2 48 Cluster 2 52 8 Cluster 3 244 86 Cluster 1: High spenders in Retail channel (Channel 2) tend to spend on Grocery, Milk and Det_Paper categories Cluster 2: High spenders in Horeca channel (Channel 1) tend to spend higher on Fresh product category Cluster 3: Low spenders
- 7. Appendix ● Observation: If we run Kmeans with Grocery, Detergent_Paper and Milk, we can capture 97% variability in data