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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 unsupervise…

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项目 3: 非监督学习

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安装

这个项目要求使用 Python 2.7 并且需要安装下面这些python包:

你同样需要安装好相应软件使之能够运行Jupyter Notebook

优达学城推荐学生安装 Anaconda, 这是一个已经打包好的python发行版,它包含了我们这个项目需要的所有的库和软件。

代码

初始代码包含在 customer_segments.ipynb 这个notebook文件中。这里面有一些代码已经实现好来帮助你开始项目,但是为了完成项目,你还需要实现附加的功能。

运行

在命令行中,确保当前目录为 customer_segments.ipynb 文件夹的最顶层(目录包含本 README 文件),运行下列命令:

jupyter notebook customer_segments.ipynb

​这会启动 Jupyter Notebook 并把项目文件打开在你的浏览器中。

数据

​这个项目的数据包含在 customers.csv 文件中。你能在UCI 机器学习信息库页面中找到更多信息。

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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 unsupervise…

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