My Second Project in Machine Learning Basics Nanodegree

Unsupervised Learning

Project: Creating Customer Segments

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Table Of Contents:

• Description
  
  • About the project
    
  • What needs to be done
    
  • Why this project
    
• Data
  
  • Files
    
  • Dataset file
    
• Loading Project
  
  • Requirements
    
  • Execution
    
• Conclusion
  
  • What I learned
    
  • Evaluation
  • Results

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Description

About the project

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.

What needs to be done

In this project I have applied 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 explored the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, I preprocessed the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, I applied PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, I compared the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.

Why this project

This project is designed to give us 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.

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Data

Files

This project contains 3 files and 1 folder:

• report.ipynb: This is the main file where I have performed my work on the project.
• data.csv: The project dataset. I have loaded this data in the notebook.
• visuals.py: This Python script provides supplementary visualizations for the project.
• export/ : Folder containing HTML and PDF version file of notebook.
• plots/ : Contains images of all the plots that are displayed in report.ipynb file.

Template code is provided in the report.ipynb notebook file. I have also been required to use the included visuals.py Python file and the data.csv dataset file to complete my work. While some code has already been implemented to get me started, I 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 to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.

Dataset file

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)

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Loading Project

Requirements

This project requires Python 3 and the following Python libraries installed:

• Python 3.6.5
• NumPy
• Pandas
• matplotlib
• scikit-learn
• seaborn

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.

Execution

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

ipython notebook report.ipynb

or

jupyter notebook report.ipynb

or if you have 'Jupyter Lab' installed

jupyter lab

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

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Conclusion

What I learned

• 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.

Evaluation

My project was reviewed by a Udacity reviewer against the Creating Customer Segments project rubric. All criteria found in the rubric must be meeting specifications for me to pass.

Results

My Project Review by an Udacity Reviewer

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