Project: Creating Customer Segments

Unsupervised Learning

Summary

Main

• customer_segments.ipynb (html)

Content

• Explore data
  • Visualize and observe feature relevance

    

• Preprocess data
  • Scale skewed continuous features
  • Detect and clear outliers
• Transform features
  • Apply principal component analysis (PCA)

    

  • Dimensionality reduction using PCA
  • Visualize a biplot

    

• Cluster
  • Metric: silhouette coefficient ( sklearn.metrics.silhouette_score )
  • Evaluate the Guassian mixture model(GMM) with varying number of clusters
    • Number of clusters = 2, score = 0.4474
    • Number of clusters = 3, score = 0.3612
    • Number of clusters = 4, score = 0.3040
  • Train the optimized GMM
  • Visualize clusters

    

  • Evaluate performance comparing with original underlying distributions

    

Project Information

Install

This project requires Python 2.7 and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn

You will also need to have software installed to run and execute a Jupyter Notebook

Run

In a terminal or command window, navigate to the top-level project directory (that contains this README) and run the following commands. This will open the Jupyter Notebook and project file in your browser.

ipython notebook customer_segments.ipynb

Data

The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the UCI Machine Learning Repository.

Note (m.u.) is shorthand for monetary units.

Features 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: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal) 8) Region: {Lisbon - 1, Oporto - 2, or Other - 3} (Nominal)