Content: Unsupervised Learning

Project: Creating Customer Segments

Project Overview

In this project I 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. I first explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, I preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, I apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, I compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes. This project was completed as part of the Udacity Machine Learning Nanodegree Program.

Project Highlights

This project is designed to give me a hands-on experience with unsupervised learning and work towards developing conclusions for a potential client on a real-world dataset. Many companies today collect vast amounts of data on customers and clientele, and have a strong desire to understand the meaningful relationships hidden in their customer base. Being equipped with this information can assist a company engineer future products and services that best satisfy the demands or needs of their customers.

Things I 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.

Description

A wholesale distributor recently tested a change to their delivery method for some customers, by moving from a morning delivery service five days a week to a cheaper evening delivery service three days a week. Initial testing did not discover any significant unsatisfactory results, so they implemented the cheaper option for all customers. Almost immediately, the distributor began getting complaints about the delivery service change and customers were canceling deliveries, losing the distributor more money than what was being saved. You've been hired by the wholesale distributor to find what types of customers they have to help them make better, more informed business decisions in the future. Your task is to use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.

Software and Libraries

This project requires Python 2.7 and uses the following software and Python libraries:

• NumPy
• pandas
• scikit-learn
• matplotlib

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.

Starting the Project

For this assignment, you can find the customer_segments folder containing the necessary project files on the Machine Learning projects GitHub, under the projects folder. You may download all of the files for projects we'll use in this Nanodegree program directly from this repo. Please make sure that you use the most recent version of project files when completing a project!

This project contains three files:

• customer_segments.ipynb: This is the main file where you will be performing your work on the project.
• customers.csv: The project dataset. You'll load this data in the notebook.
• visuals.py: This Python script provides supplementary visualizations for the project. Do not modify.

In the Terminal or Command Prompt, navigate to the folder containing the project files, and then use the command jupyter notebook customer_segments.ipynb to open up a browser window or tab to work with your notebook. Alternatively, you can use the command jupyter notebook or ipython notebook and navigate to the notebook file in the browser window that opens. Follow the instructions in the notebook and answer each question presented to successfully complete the project. A README file has also been provided with the project files which may contain additional necessary information or instruction for the project.

Code

Template code is provided in the customer_segments.ipynb notebook file. You will also be required to use the included visuals.py Python file and the customers.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.

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)