In this project I 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. I will first explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, I will preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, I will apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, I will compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.
This project is designed to give 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 will 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.
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. I'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. My task is to use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.
This project uses the following software and Python libraries:
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.
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 i will be performing my work on the project.customers.csv: The project dataset. I'll load this data in the notebook.visuals.py: This Python script provides supplementary visualizations for the project.
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 my 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.