Scikit Learn Incorporation - p.16 Data Analysis with Python and Pandas Tutorial
306In this Data Analysis with Pandas and Python tutorial series, we're going to show how quickly we can take our Pandas dataset in the dataframe and convert it to, for example, a numpy array, which can then be fed through a variety of other data analysis Python modules. The example that we're going to use here is Scikit-Learn, or SKlearn. In order to do this, you will need to install it:
pip install sklearn
From here, we're almost already done. For machine learning to take place, at least in the supervised form, we need only a couple things. First, we need "features." In our case, features are things like current HPI, maybe the GDP, and so on. Then you have "labels." Labels are assigned to the feature "sets," where a feature set is the collective GDP, HPI, and so on for any given "label." Our label, in this case, is either a 1 or a 0, where 1 means the HPI increased in the future, and a 0 means it did not.
Sample code and text-based tutorial: http://pythonprogramming.net/scikit-learn-sklearn-machine-learning-data-analysis-python-pandas-tutorial/
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