wholesale-clustering

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Purpose

• Clasifying sales data with given 8 features and giving appropriate sales idea with the result.
• Use unsupervised learning – Hierarchical Clustering, DBSCAN, K-Means Clustering.

File Directory

wholesale-clustering
├── data
│   └── Wholesale customers data.csv               # 연간 고객 판매량 데이터 csv
├── wholesale-kmeans.ipynb                         # 반소희, 이서린 : K-Means Clustering
├── wholesale_hierarchical.ipynb                   # 장아연 :  Hierarchical Clustering
└── wholesale_DBScan.ipynb                         # 이희원, 최지민 : DBSCAN

Instructions

1. Open colab by clicking links below
   • K-means (link)
   • Hierarchical (link)
   • DBSCAN (link)
2. Set Hardware accelerator to GPU (Runtime - Change runtime type - Hardware accelerator : GPU)
3. Run all codes (shortcut: cmd/ctrl + F9)

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Methods

1. load data

2. check missing value

   • As there was no missing value, skipped this step.

3. preprosessing

   • numeric feature
     • normalization
       • For numeric features, there are some well known data preprosessing, standardlization and normalization.
       • Our numeric features need to be normalized so we used minmax scaler for min-max normalization.
     • outliers
       • After normalization, we check byplot of each features and we can find out there are some outliers-based on IQR.
       • So we replaced outliers with Q3 value(75% of max value) of each feature.
   • categorical feature
     • Use one-hot encoding to train machine learning model.

4. Find Optimal K

   • Elbow Method
   • Silhoutte Coefficient
     • To find our optimal K, we used 2 methods mentioned above.
     • For k in range 2 to 15, we drawed graph to check it visually.
     • Through these two method, we could get Optimal K = 6 in common.

5. Clustering with 6 clusters

   • Use KMeans, DBSCAN, Hierarchical clustering to clustering data.
   • Preceed clustering with k = 6.
   • There are 2 nominal features, Region and Channel, and we can get 6 combinations from those features.
   • And we now can see that those 6 combinations corrrspond to 6 labels one-on-one.

Analysis Clusters

• 6 clusters are created.

  KMeans		Hierarchical

  Clusters

• Result of each cluster.

  Results of wholesale clustering

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Reasons for DBSCAN failure

• DBSCAN is sensitive to density of data.
• Data in each column are gathered in short range of sales amount (7000-12000).

  Density plot

• Data are gathered in high and even density.
• It is hard to find appropriate epsilon and MinPts.
  • Because data are gathered in high density, slight change in hyperparameter occurs huge change in clustering result.