Reviewed unstructured data to understand the patterns and natural categories that the data fits into. 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 unsupervised analysis.
This project requires Python 2.7 and the following Python libraries installed:
You will also need to have software installed to run and execute an iPython Notebook
You may install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.
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 customer_segments.ipynb
jupyter notebook customer_segments.ipynb
This will open the iPython Notebook software and project file in your browser.
The dataset used in this project is included as customers.csv. You can find more information on this dataset on the UCI Machine Learning Repository page.