Content: Unsupervised Learning

Project: Creating Customer Segments

In this project we will apply 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.

Things to learn by completing this project:

• How to apply preprocessing techniques such as feature scaling and outlier detection.
• How to interpret data points that have been scaled, transformed, or reduced from PCA.
• How to analyze PCA dimensions and construct a new feature space.
• How to optimally cluster a set of data to find hidden patterns in a dataset.
• How to assess information given by cluster data and use it in a meaningful way.

Part of Udacity's Machine Learning Nanodegree

Project resides in customer_segments.ipynb

Environment

This project runs on Python 3.5 and uses the following libraries:

• NumPy
• Pandas
• matplotlib
• scikit-learn
• Jupyter Notebook

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)