Content: Unsupervised Learning

Project: Creating Customer Segments

In this project I Reviewed unstructured data to understand the patterns and natural categories that the data fits into. I Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of an unsupervised analysis.

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

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

Run

In a terminal or command window, navigate to the top-level project directory customer_segments/ (that contains this README) and run one of the following commands:

ipython notebook customer_segments.ipynb

or

jupyter notebook customer_segments.ipynb

This will open the Jupyter Notebook software and project file in your browser.

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)