https://github.com/tetsumichiumada/customer_segments
Identify customers by clustering them
https://github.com/tetsumichiumada/customer_segments
machine-learning python scikit-learn unsupervised-learning
Last synced: about 2 months ago
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Identify customers by clustering them
- Host: GitHub
- URL: https://github.com/tetsumichiumada/customer_segments
- Owner: TetsumichiUmada
- Created: 2017-06-01T00:49:41.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2017-06-01T01:38:56.000Z (about 9 years ago)
- Last Synced: 2025-06-14T12:43:27.566Z (about 1 year ago)
- Topics: machine-learning, python, scikit-learn, unsupervised-learning
- Language: HTML
- Homepage:
- Size: 746 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Creating Customer Segments
The main objective of this project is to apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.
## Software Requirements and Libraries
### Install
This project requires **Python 2.7** and the following Python libraries installed:
- [NumPy](http://www.numpy.org/)
- [Pandas](http://pandas.pydata.org)
- [matplotlib](http://matplotlib.org/)
- [scikit-learn](http://scikit-learn.org/stable/)
You will also need to have software installed to run and execute an [iPython Notebook](http://ipython.org/notebook.html)
Udacity recommends our students install [Anaconda](https://www.continuum.io/downloads), a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.
### Code
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.
### Run
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.
## Data
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](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers) page.
This project is a part of the Machine Learning Engineer Nanodegree program at [Udacity](https://www.udacity.com/).