Creating Customer Segments

In this project I applied unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data. I first explored the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, I preprocessed the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, I applied PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, I compared the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.

The main techniques used:

  • Clustering
  • More Clustering
  • Feature Scaling
  • Feature Selection
  • PCA
  • Feature Transformation

You can see the code(iPython notebook) there.

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