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.
Template code is provided in the notebook customer_segments.ipynb notebook file. Additional supporting code can be found in renders.py. 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.
###Goal
Analyze a dataset containing data on various customers' annual spending amounts (reported in monetary units) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.
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.