In this project you 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. You will first explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, you will preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, you will apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, you 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 uses the following software and Python libraries:
- Python 2.7
- NumPy
- pandas
- matplotlib
- scikit-learn (v0.17)
You will also need to have software installed to run and execute a Jupyter Notebook.
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